AI Glossary
Every AI term you've heard but were too afraid to Google — explained in plain English, no computer science degree required.
AI is full of jargon. We cut through it so you can have informed conversations, make better decisions, and stop nodding along when someone says 'large language model'.
AI Washing
When a company exaggerates or falsely claims that its product uses AI to appear more innovative or attract investment. Similar to 'greenwashing' in sustainability. As AI becomes a buzzword, AI washing is increasingly common — it's worth asking vendors exactly how AI is used in their product, not just whether it is.
Algorithm
A set of step-by-step instructions a computer follows to complete a task or solve a problem. Think of it like a recipe — the computer follows each step in order to produce a result. Most AI tools are built on complex algorithms that process data and generate outputs.
Artificial Intelligence (AI)
Technology that enables computers to perform tasks that normally require human intelligence — things like understanding language, recognizing images, making decisions, and learning from experience. AI is an umbrella term that covers many different technologies and approaches.
Automation
Using technology to perform tasks with little or no human input. In a business context, automation means setting up systems that handle repetitive work automatically — like sending follow-up emails, updating spreadsheets, or routing customer enquiries — so your team can focus on higher-value work.
Agentic AI
AI that can take actions and make decisions autonomously to complete a goal — not just answer questions, but actually do things like browse the web, send emails, or interact with software on your behalf. Agentic AI is one of the fastest-growing areas of AI development.
AI-Native
A product, business, or workflow that is built from the ground up with AI at its core — rather than having AI bolted on as an afterthought. An AI-native business uses AI as a fundamental part of how it operates, not just as an occasional tool. Increasingly, being AI-native is a competitive advantage.
API (Application Programming Interface)
A way for two software applications to talk to each other. When we integrate an AI tool with your existing software, we're usually using APIs to connect them. You don't need to understand how APIs work — just know they're the plumbing that makes integrations possible.
ChatGPT
The AI assistant built by OpenAI and one of the most widely used AI tools in the world. ChatGPT can write, summarize, answer questions, draft emails, generate ideas, and much more — all through a simple chat interface. It runs on OpenAI's GPT models and is available free or via a paid plan. For most small businesses, it's the best starting point for exploring what AI can do.
Claude
An AI assistant built by Anthropic, widely regarded as one of the best alternatives to ChatGPT. Claude is known for nuanced writing, long-document analysis, and following complex instructions carefully. It's designed with a strong focus on safety and honesty. Available at claude.ai with a free tier and paid plans.
Bot
Short for 'robot' — a software program that performs automated tasks. In business, bots are commonly used for customer service (chatbots), data collection, and repetitive admin tasks. Not all bots use AI, but AI-powered bots are far more capable than simple rule-based ones.
Benchmark
A standardised test used to measure and compare the performance of AI models. When you see claims like 'our AI scores 90% on X benchmark', it means the model was tested against a standard set of problems. Benchmarks help compare different AI tools, though real-world performance can vary.
Black Box
A system where you can see the inputs and outputs but not what happens in between. Most AI models are black boxes — you give them a prompt and get a response, but the internal reasoning is not visible or easily explainable. This is why 'explainable AI' is an active area of research, particularly in regulated industries like finance and healthcare.
Chatbot
A software program designed to simulate conversation with humans, typically via text. Modern AI-powered chatbots (like those built on ChatGPT) can understand natural language, answer complex questions, and handle customer service tasks around the clock — far beyond the simple 'press 1 for billing' bots of the past.
Context Window
The amount of text an AI model can 'see' and consider at one time. Think of it like short-term memory — the larger the context window, the more of a conversation or document the AI can take into account when generating a response. Larger context windows generally produce more coherent, relevant outputs.
Copilot
An AI assistant that works alongside a human to help them do their job better — not replacing them, but augmenting their capabilities. Microsoft's Copilot (built into Office 365) is a well-known example. The term reflects the idea that the human is still in control; the AI is just helping navigate.
Data
Information in a form that computers can process. AI systems learn from data — the more relevant, high-quality data an AI is trained on, the better it tends to perform. In a business context, your data includes things like customer records, transaction history, emails, and documents.
Deepfake
AI-generated video, audio, or images that realistically depict someone saying or doing something they never actually said or did. Deepfakes are created using deep learning techniques and have become increasingly convincing. As a business owner, it's important to be aware of deepfakes — both as a potential security risk (e.g. fake audio of a CEO authorising a transfer) and as a reputational concern.
Deep Learning
A type of machine learning that uses neural networks with many layers to learn from large amounts of data. Deep learning powers many of the most impressive AI capabilities today — including image recognition, speech recognition, and large language models like ChatGPT.
Embedding
A way of representing text, images, or other data as numbers so that an AI can understand relationships between concepts. Embeddings allow AI to understand that 'dog' and 'puppy' are related, or that 'invoice' and 'bill' mean similar things — even without being explicitly told.
Emergent Behavior
Unexpected capabilities that appear in AI models as they grow larger or are trained on more data — abilities that weren't explicitly programmed or anticipated. For example, large language models spontaneously developed the ability to do basic arithmetic and translate languages without being specifically trained to do so. Emergent behavior is one of the reasons AI development can be unpredictable.
Fine-Tuning
The process of taking a pre-trained AI model and training it further on a specific dataset to make it better at a particular task. For example, fine-tuning a general language model on your company's documents and tone of voice so it writes in your style and understands your business context.
Frontier Model
The most advanced AI models available at any given time — the cutting edge of what's technically possible. GPT-4o, Claude 3.5 Sonnet, and Gemini Ultra are examples of frontier models. The term is used in AI policy and safety discussions to describe models powerful enough to potentially pose novel risks alongside their capabilities.
Foundation Model
A large AI model trained on a broad dataset that can be adapted for many different tasks. GPT-4, Claude, and Gemini are all foundation models. They're called 'foundation' models because they serve as the base that other, more specialized AI applications are built on top of.
Gemini
Google's flagship AI assistant and language model, available at gemini.google.com. Gemini integrates natively with Google Workspace — Gmail, Docs, Sheets, Drive — making it a strong choice for businesses already running on Google tools. It competes directly with ChatGPT and Claude and is available with a free tier and paid Google One AI Premium plan.
Grok
An AI assistant built by xAI, Elon Musk's AI company, and integrated into the X (formerly Twitter) platform. Grok is notable for having real-time access to information posted on X and for taking a less filtered approach to responses compared to some competitors. Available to X Premium subscribers and via the Grok standalone app.
Generative AI
AI that can create new content — text, images, audio, video, code — rather than just analysing or classifying existing content. ChatGPT generating a business proposal, DALL-E creating an image from a description, and GitHub Copilot writing code are all examples of generative AI in action.
GPT (Generative Pre-trained Transformer)
The type of AI model that powers ChatGPT, developed by OpenAI. GPT models are trained on vast amounts of text data and can generate human-like text, answer questions, summarize documents, write code, and much more. The 'transformer' part refers to the underlying architecture that makes modern AI so powerful.
Guardrails
Rules, filters, and safety mechanisms built into AI systems to prevent them from producing harmful, offensive, or inappropriate outputs. Guardrails are why AI assistants refuse certain requests. When building custom AI tools for businesses, we also add business-specific guardrails — for example, preventing a customer service bot from making promises the business can't keep.
Hallucination
When an AI confidently produces information that is factually incorrect or completely made up. This is one of the most important limitations of current AI tools to understand. AI doesn't 'know' things the way humans do — it predicts likely text, which can sometimes result in plausible-sounding but wrong answers. Always verify important facts.
Human-in-the-Loop
A system design where humans are involved in reviewing, approving, or correcting AI outputs before they're acted on. For most business applications, keeping a human in the loop is best practice — especially for anything customer-facing, financial, or legally sensitive.
Hyperparameter
Settings that control how an AI model is trained — like how fast it learns, how many training rounds it goes through, or how large its layers are. Unlike regular parameters (which the model learns automatically), hyperparameters are set by the people building the model. Getting them right is part of the craft of AI development.
Inference
The process of using a trained AI model to generate outputs from new inputs. When you type a question into ChatGPT and it responds, that's inference — the model is applying what it learned during training to your specific input. Training happens once; inference happens every time you use the tool.
Integration
Connecting two or more software systems so they can share data and work together. In AI, integration typically means connecting an AI tool to your existing business software — like linking a chatbot to your CRM, or connecting an automation tool to your booking system.
Jailbreak
An attempt to bypass the safety guidelines or restrictions built into an AI model — usually by crafting clever prompts that trick the AI into ignoring its rules. AI companies work continuously to prevent jailbreaks. As a business owner, it's worth knowing this exists so you understand why AI tools sometimes refuse certain requests — those guardrails are there for good reason.
Knowledge Base
A structured collection of information that an AI can draw on to answer questions. When we build a custom AI assistant for your business, we often create a knowledge base containing your FAQs, product details, policies, and procedures — so the AI gives accurate, business-specific answers rather than generic ones.
Large Language Model (LLM)
An AI model trained on enormous amounts of text data that can understand and generate human language. ChatGPT, Claude, and Gemini are all LLMs. They're called 'large' because of the sheer scale of data they're trained on and the number of parameters (internal settings) they contain — often in the billions.
Llama
Meta's family of open-source large language models, released for anyone to download, run, and build on. Unlike ChatGPT or Claude, Llama models can be run locally on your own hardware without sending data to a third-party server — making them popular for privacy-sensitive applications and businesses that want more control over their AI stack.
Machine Learning (ML)
A branch of AI where systems learn from data and improve their performance over time without being explicitly programmed for every scenario. Instead of following rigid rules, machine learning models identify patterns in data and use those patterns to make predictions or decisions.
Multimodal AI
AI that can process and generate multiple types of content — text, images, audio, and video — rather than just one. GPT-4o is multimodal: you can show it a photo and ask a question about it, or have it describe an image. Multimodal AI opens up a much wider range of business applications than text-only models.
Model
In AI, a model is the trained system that takes inputs and produces outputs. When people refer to 'the model', they mean the AI itself — the mathematical structure that has been trained on data. Different models have different strengths, weaknesses, and use cases.
Natural Language Processing (NLP)
The branch of AI focused on enabling computers to understand, interpret, and generate human language. NLP powers chatbots, voice assistants, translation tools, sentiment analysis, and much more. When an AI understands what you're asking in plain English, that's NLP at work.
Neural Network
A type of AI system loosely inspired by the structure of the human brain, made up of layers of interconnected nodes that process information. Neural networks are the foundation of most modern AI — including image recognition, speech recognition, and large language models.
No-Code / Low-Code
Tools and platforms that allow people to build software applications and automations without writing traditional code. Many AI tools are now available in no-code or low-code formats, making them accessible to business owners without technical backgrounds. Zapier and Make are popular examples.
OpenAI
The company behind ChatGPT, GPT-4, and DALL-E. OpenAI is one of the most influential AI research organizations in the world. Founded in 2015, it has become the most recognized name in consumer AI and provides the underlying technology for many third-party AI tools and applications.
Overfitting
When an AI model learns its training data too well — including the noise and quirks — and as a result performs poorly on new, real-world data. Think of it like a student who memorises past exam papers word-for-word but can't answer a question that's phrased slightly differently. Overfitting is a common challenge in building reliable AI systems.
Parameters
The internal settings of an AI model that are adjusted during training to improve performance. A model with more parameters is generally more capable but also more expensive to run. GPT-4 is estimated to have hundreds of billions of parameters — which is part of why it's so capable.
Prompt
The input you give to an AI model — the question, instruction, or context that tells the AI what you want it to do. Writing effective prompts (prompt engineering) is a skill that significantly affects the quality of AI outputs. A well-crafted prompt can be the difference between a mediocre and an excellent result.
Prompt Engineering
The practice of designing and refining prompts to get better outputs from AI models. It involves understanding how AI models interpret instructions and structuring your inputs to guide the model toward the response you want. Good prompt engineering is one of the most practical AI skills a business owner can develop.
Prompt Injection
A type of attack where a malicious user crafts inputs designed to override an AI system's instructions and make it behave in unintended ways. For example, a user might try to trick a customer service chatbot into ignoring its rules by embedding hidden instructions in their message. It's a real security concern for businesses deploying AI-facing tools — and something we account for when building AI systems for clients.
Query
A question or request submitted to an AI system, search engine, or database. In everyday AI use, a query is essentially what you type into ChatGPT or a search bar. In more technical contexts, queries are used to retrieve specific data from databases. The quality of your query directly affects the quality of the answer you get back.
RAG (Retrieval-Augmented Generation)
A technique that combines an AI language model with a search system, allowing the AI to retrieve relevant information from a specific knowledge base before generating a response. RAG is how you build AI tools that can answer questions about your specific business — your products, policies, FAQs — rather than just general knowledge.
Sentiment Analysis
An AI technique that identifies and categorises the emotional tone of text — positive, negative, or neutral. Businesses use sentiment analysis to monitor customer reviews, social media mentions, and support tickets to understand how customers feel about their products or services at scale.
Shadow AI
The use of AI tools by employees without the knowledge or approval of their organization's IT or management team. Just as 'shadow IT' described unauthorised software use, shadow AI is a growing concern — staff may be pasting sensitive client data into ChatGPT or using unapproved AI tools, creating data privacy and security risks. Having a clear AI policy in your business is increasingly important.
System Prompt
A set of instructions given to an AI model before a conversation begins, used to define its persona, rules, and behavior. When we build a custom AI chatbot for a business, the system prompt is where we tell it who it is, what it can and can't do, and how it should respond to customers.
Token
The basic unit of text that AI language models process. A token is roughly equivalent to a word or part of a word — 'hamburger' might be split into two tokens, while 'the' is one. AI models have token limits for inputs and outputs, and usage is often billed per token. Understanding tokens helps you use AI tools more efficiently.
Training
The process of teaching an AI model by exposing it to large amounts of data and adjusting its parameters to improve performance. Training a large AI model requires enormous computing power and can take weeks or months. Most businesses use pre-trained models rather than training their own from scratch.
Transformer
The underlying architecture that powers most modern AI language models, including GPT, Claude, and Gemini. Introduced by Google in 2017, the transformer architecture revolutionised AI by enabling models to process and understand context across long sequences of text far more effectively than previous approaches.
Use Case
A specific, practical application of a technology to solve a real problem. In AI, a use case might be 'using AI to automatically respond to common customer enquiries' or 'using AI to summarize meeting notes'. Identifying the right use cases for your business is the first step in any successful AI implementation.
Vector Database
A type of database designed to store and search data in the format that AI models use (vectors/embeddings). Vector databases are a key component of RAG systems — they allow AI tools to quickly find the most relevant information from a large knowledge base when answering a question.
Vibe Coding
A term coined in 2025 describing the practice of building software by describing what you want in plain English to an AI — and letting the AI write the code for you. Instead of learning to code, you 'vibe' with the AI: you describe the idea, review what it builds, give feedback, and iterate. Tools like Cursor, GitHub Copilot, and platforms like the one this website was built on make vibe coding accessible to non-developers. It's one of the most significant shifts in how software gets made.
Voice AI
AI systems that can understand and generate spoken language — enabling voice-based interactions with software. Voice AI powers smart speakers (Alexa, Siri, Google Assistant), AI phone receptionists, and voice-enabled customer service tools. For small businesses, voice AI can handle inbound calls, answer common questions, and route enquiries — 24 hours a day.
Workflow
A sequence of steps or tasks that make up a business process. In the context of AI and automation, a workflow is the series of actions that get triggered and executed automatically — for example, a new lead fills in a form, which triggers an AI to qualify them, send a welcome email, and add them to your CRM.
xAI
The AI company founded by Elon Musk in 2023, responsible for building Grok. xAI's stated mission is to understand the true nature of the universe through AI research. It's one of the newer major players in the AI space and is notable for its integration with the X (formerly Twitter) platform.
Yield (AI Output Quality)
In AI contexts, yield refers to the quality and usefulness of the outputs an AI produces relative to the inputs given. High-yield prompting means getting consistently valuable, accurate, and relevant responses. Improving your yield is largely about better prompts, clearer context, and choosing the right model for the task.
Zero-Shot Learning
The ability of an AI model to perform a task it hasn't been specifically trained on, using only a description or instruction. For example, asking ChatGPT to classify customer feedback into categories it's never seen before. Zero-shot capability is one of the things that makes modern LLMs so versatile for business use.
AI Washing
When a company exaggerates or falsely claims that its product uses AI to appear more innovative or attract investment. Similar to 'greenwashing' in sustainability. As AI becomes a buzzword, AI washing is increasingly common — it's worth asking vendors exactly how AI is used in their product, not just whether it is.
Algorithm
A set of step-by-step instructions a computer follows to complete a task or solve a problem. Think of it like a recipe — the computer follows each step in order to produce a result. Most AI tools are built on complex algorithms that process data and generate outputs.
Artificial Intelligence (AI)
Technology that enables computers to perform tasks that normally require human intelligence — things like understanding language, recognizing images, making decisions, and learning from experience. AI is an umbrella term that covers many different technologies and approaches.
Automation
Using technology to perform tasks with little or no human input. In a business context, automation means setting up systems that handle repetitive work automatically — like sending follow-up emails, updating spreadsheets, or routing customer enquiries — so your team can focus on higher-value work.
Agentic AI
AI that can take actions and make decisions autonomously to complete a goal — not just answer questions, but actually do things like browse the web, send emails, or interact with software on your behalf. Agentic AI is one of the fastest-growing areas of AI development.
AI-Native
A product, business, or workflow that is built from the ground up with AI at its core — rather than having AI bolted on as an afterthought. An AI-native business uses AI as a fundamental part of how it operates, not just as an occasional tool. Increasingly, being AI-native is a competitive advantage.
API (Application Programming Interface)
A way for two software applications to talk to each other. When we integrate an AI tool with your existing software, we're usually using APIs to connect them. You don't need to understand how APIs work — just know they're the plumbing that makes integrations possible.
ChatGPT
The AI assistant built by OpenAI and one of the most widely used AI tools in the world. ChatGPT can write, summarize, answer questions, draft emails, generate ideas, and much more — all through a simple chat interface. It runs on OpenAI's GPT models and is available free or via a paid plan. For most small businesses, it's the best starting point for exploring what AI can do.
Claude
An AI assistant built by Anthropic, widely regarded as one of the best alternatives to ChatGPT. Claude is known for nuanced writing, long-document analysis, and following complex instructions carefully. It's designed with a strong focus on safety and honesty. Available at claude.ai with a free tier and paid plans.
Bot
Short for 'robot' — a software program that performs automated tasks. In business, bots are commonly used for customer service (chatbots), data collection, and repetitive admin tasks. Not all bots use AI, but AI-powered bots are far more capable than simple rule-based ones.
Benchmark
A standardised test used to measure and compare the performance of AI models. When you see claims like 'our AI scores 90% on X benchmark', it means the model was tested against a standard set of problems. Benchmarks help compare different AI tools, though real-world performance can vary.
Black Box
A system where you can see the inputs and outputs but not what happens in between. Most AI models are black boxes — you give them a prompt and get a response, but the internal reasoning is not visible or easily explainable. This is why 'explainable AI' is an active area of research, particularly in regulated industries like finance and healthcare.
Chatbot
A software program designed to simulate conversation with humans, typically via text. Modern AI-powered chatbots (like those built on ChatGPT) can understand natural language, answer complex questions, and handle customer service tasks around the clock — far beyond the simple 'press 1 for billing' bots of the past.
Context Window
The amount of text an AI model can 'see' and consider at one time. Think of it like short-term memory — the larger the context window, the more of a conversation or document the AI can take into account when generating a response. Larger context windows generally produce more coherent, relevant outputs.
Copilot
An AI assistant that works alongside a human to help them do their job better — not replacing them, but augmenting their capabilities. Microsoft's Copilot (built into Office 365) is a well-known example. The term reflects the idea that the human is still in control; the AI is just helping navigate.
Data
Information in a form that computers can process. AI systems learn from data — the more relevant, high-quality data an AI is trained on, the better it tends to perform. In a business context, your data includes things like customer records, transaction history, emails, and documents.
Deepfake
AI-generated video, audio, or images that realistically depict someone saying or doing something they never actually said or did. Deepfakes are created using deep learning techniques and have become increasingly convincing. As a business owner, it's important to be aware of deepfakes — both as a potential security risk (e.g. fake audio of a CEO authorising a transfer) and as a reputational concern.
Deep Learning
A type of machine learning that uses neural networks with many layers to learn from large amounts of data. Deep learning powers many of the most impressive AI capabilities today — including image recognition, speech recognition, and large language models like ChatGPT.
Embedding
A way of representing text, images, or other data as numbers so that an AI can understand relationships between concepts. Embeddings allow AI to understand that 'dog' and 'puppy' are related, or that 'invoice' and 'bill' mean similar things — even without being explicitly told.
Emergent Behavior
Unexpected capabilities that appear in AI models as they grow larger or are trained on more data — abilities that weren't explicitly programmed or anticipated. For example, large language models spontaneously developed the ability to do basic arithmetic and translate languages without being specifically trained to do so. Emergent behavior is one of the reasons AI development can be unpredictable.
Fine-Tuning
The process of taking a pre-trained AI model and training it further on a specific dataset to make it better at a particular task. For example, fine-tuning a general language model on your company's documents and tone of voice so it writes in your style and understands your business context.
Frontier Model
The most advanced AI models available at any given time — the cutting edge of what's technically possible. GPT-4o, Claude 3.5 Sonnet, and Gemini Ultra are examples of frontier models. The term is used in AI policy and safety discussions to describe models powerful enough to potentially pose novel risks alongside their capabilities.
Foundation Model
A large AI model trained on a broad dataset that can be adapted for many different tasks. GPT-4, Claude, and Gemini are all foundation models. They're called 'foundation' models because they serve as the base that other, more specialized AI applications are built on top of.
Gemini
Google's flagship AI assistant and language model, available at gemini.google.com. Gemini integrates natively with Google Workspace — Gmail, Docs, Sheets, Drive — making it a strong choice for businesses already running on Google tools. It competes directly with ChatGPT and Claude and is available with a free tier and paid Google One AI Premium plan.
Grok
An AI assistant built by xAI, Elon Musk's AI company, and integrated into the X (formerly Twitter) platform. Grok is notable for having real-time access to information posted on X and for taking a less filtered approach to responses compared to some competitors. Available to X Premium subscribers and via the Grok standalone app.
Generative AI
AI that can create new content — text, images, audio, video, code — rather than just analysing or classifying existing content. ChatGPT generating a business proposal, DALL-E creating an image from a description, and GitHub Copilot writing code are all examples of generative AI in action.
GPT (Generative Pre-trained Transformer)
The type of AI model that powers ChatGPT, developed by OpenAI. GPT models are trained on vast amounts of text data and can generate human-like text, answer questions, summarize documents, write code, and much more. The 'transformer' part refers to the underlying architecture that makes modern AI so powerful.
Guardrails
Rules, filters, and safety mechanisms built into AI systems to prevent them from producing harmful, offensive, or inappropriate outputs. Guardrails are why AI assistants refuse certain requests. When building custom AI tools for businesses, we also add business-specific guardrails — for example, preventing a customer service bot from making promises the business can't keep.
Hallucination
When an AI confidently produces information that is factually incorrect or completely made up. This is one of the most important limitations of current AI tools to understand. AI doesn't 'know' things the way humans do — it predicts likely text, which can sometimes result in plausible-sounding but wrong answers. Always verify important facts.
Human-in-the-Loop
A system design where humans are involved in reviewing, approving, or correcting AI outputs before they're acted on. For most business applications, keeping a human in the loop is best practice — especially for anything customer-facing, financial, or legally sensitive.
Hyperparameter
Settings that control how an AI model is trained — like how fast it learns, how many training rounds it goes through, or how large its layers are. Unlike regular parameters (which the model learns automatically), hyperparameters are set by the people building the model. Getting them right is part of the craft of AI development.
Inference
The process of using a trained AI model to generate outputs from new inputs. When you type a question into ChatGPT and it responds, that's inference — the model is applying what it learned during training to your specific input. Training happens once; inference happens every time you use the tool.
Integration
Connecting two or more software systems so they can share data and work together. In AI, integration typically means connecting an AI tool to your existing business software — like linking a chatbot to your CRM, or connecting an automation tool to your booking system.
Jailbreak
An attempt to bypass the safety guidelines or restrictions built into an AI model — usually by crafting clever prompts that trick the AI into ignoring its rules. AI companies work continuously to prevent jailbreaks. As a business owner, it's worth knowing this exists so you understand why AI tools sometimes refuse certain requests — those guardrails are there for good reason.
Knowledge Base
A structured collection of information that an AI can draw on to answer questions. When we build a custom AI assistant for your business, we often create a knowledge base containing your FAQs, product details, policies, and procedures — so the AI gives accurate, business-specific answers rather than generic ones.
Large Language Model (LLM)
An AI model trained on enormous amounts of text data that can understand and generate human language. ChatGPT, Claude, and Gemini are all LLMs. They're called 'large' because of the sheer scale of data they're trained on and the number of parameters (internal settings) they contain — often in the billions.
Llama
Meta's family of open-source large language models, released for anyone to download, run, and build on. Unlike ChatGPT or Claude, Llama models can be run locally on your own hardware without sending data to a third-party server — making them popular for privacy-sensitive applications and businesses that want more control over their AI stack.
Machine Learning (ML)
A branch of AI where systems learn from data and improve their performance over time without being explicitly programmed for every scenario. Instead of following rigid rules, machine learning models identify patterns in data and use those patterns to make predictions or decisions.
Multimodal AI
AI that can process and generate multiple types of content — text, images, audio, and video — rather than just one. GPT-4o is multimodal: you can show it a photo and ask a question about it, or have it describe an image. Multimodal AI opens up a much wider range of business applications than text-only models.
Model
In AI, a model is the trained system that takes inputs and produces outputs. When people refer to 'the model', they mean the AI itself — the mathematical structure that has been trained on data. Different models have different strengths, weaknesses, and use cases.
Natural Language Processing (NLP)
The branch of AI focused on enabling computers to understand, interpret, and generate human language. NLP powers chatbots, voice assistants, translation tools, sentiment analysis, and much more. When an AI understands what you're asking in plain English, that's NLP at work.
Neural Network
A type of AI system loosely inspired by the structure of the human brain, made up of layers of interconnected nodes that process information. Neural networks are the foundation of most modern AI — including image recognition, speech recognition, and large language models.
No-Code / Low-Code
Tools and platforms that allow people to build software applications and automations without writing traditional code. Many AI tools are now available in no-code or low-code formats, making them accessible to business owners without technical backgrounds. Zapier and Make are popular examples.
OpenAI
The company behind ChatGPT, GPT-4, and DALL-E. OpenAI is one of the most influential AI research organizations in the world. Founded in 2015, it has become the most recognized name in consumer AI and provides the underlying technology for many third-party AI tools and applications.
Overfitting
When an AI model learns its training data too well — including the noise and quirks — and as a result performs poorly on new, real-world data. Think of it like a student who memorises past exam papers word-for-word but can't answer a question that's phrased slightly differently. Overfitting is a common challenge in building reliable AI systems.
Parameters
The internal settings of an AI model that are adjusted during training to improve performance. A model with more parameters is generally more capable but also more expensive to run. GPT-4 is estimated to have hundreds of billions of parameters — which is part of why it's so capable.
Prompt
The input you give to an AI model — the question, instruction, or context that tells the AI what you want it to do. Writing effective prompts (prompt engineering) is a skill that significantly affects the quality of AI outputs. A well-crafted prompt can be the difference between a mediocre and an excellent result.
Prompt Engineering
The practice of designing and refining prompts to get better outputs from AI models. It involves understanding how AI models interpret instructions and structuring your inputs to guide the model toward the response you want. Good prompt engineering is one of the most practical AI skills a business owner can develop.
Prompt Injection
A type of attack where a malicious user crafts inputs designed to override an AI system's instructions and make it behave in unintended ways. For example, a user might try to trick a customer service chatbot into ignoring its rules by embedding hidden instructions in their message. It's a real security concern for businesses deploying AI-facing tools — and something we account for when building AI systems for clients.
Query
A question or request submitted to an AI system, search engine, or database. In everyday AI use, a query is essentially what you type into ChatGPT or a search bar. In more technical contexts, queries are used to retrieve specific data from databases. The quality of your query directly affects the quality of the answer you get back.
RAG (Retrieval-Augmented Generation)
A technique that combines an AI language model with a search system, allowing the AI to retrieve relevant information from a specific knowledge base before generating a response. RAG is how you build AI tools that can answer questions about your specific business — your products, policies, FAQs — rather than just general knowledge.
Sentiment Analysis
An AI technique that identifies and categorises the emotional tone of text — positive, negative, or neutral. Businesses use sentiment analysis to monitor customer reviews, social media mentions, and support tickets to understand how customers feel about their products or services at scale.
Shadow AI
The use of AI tools by employees without the knowledge or approval of their organization's IT or management team. Just as 'shadow IT' described unauthorised software use, shadow AI is a growing concern — staff may be pasting sensitive client data into ChatGPT or using unapproved AI tools, creating data privacy and security risks. Having a clear AI policy in your business is increasingly important.
System Prompt
A set of instructions given to an AI model before a conversation begins, used to define its persona, rules, and behavior. When we build a custom AI chatbot for a business, the system prompt is where we tell it who it is, what it can and can't do, and how it should respond to customers.
Token
The basic unit of text that AI language models process. A token is roughly equivalent to a word or part of a word — 'hamburger' might be split into two tokens, while 'the' is one. AI models have token limits for inputs and outputs, and usage is often billed per token. Understanding tokens helps you use AI tools more efficiently.
Training
The process of teaching an AI model by exposing it to large amounts of data and adjusting its parameters to improve performance. Training a large AI model requires enormous computing power and can take weeks or months. Most businesses use pre-trained models rather than training their own from scratch.
Transformer
The underlying architecture that powers most modern AI language models, including GPT, Claude, and Gemini. Introduced by Google in 2017, the transformer architecture revolutionised AI by enabling models to process and understand context across long sequences of text far more effectively than previous approaches.
Use Case
A specific, practical application of a technology to solve a real problem. In AI, a use case might be 'using AI to automatically respond to common customer enquiries' or 'using AI to summarize meeting notes'. Identifying the right use cases for your business is the first step in any successful AI implementation.
Vector Database
A type of database designed to store and search data in the format that AI models use (vectors/embeddings). Vector databases are a key component of RAG systems — they allow AI tools to quickly find the most relevant information from a large knowledge base when answering a question.
Vibe Coding
A term coined in 2025 describing the practice of building software by describing what you want in plain English to an AI — and letting the AI write the code for you. Instead of learning to code, you 'vibe' with the AI: you describe the idea, review what it builds, give feedback, and iterate. Tools like Cursor, GitHub Copilot, and platforms like the one this website was built on make vibe coding accessible to non-developers. It's one of the most significant shifts in how software gets made.
Voice AI
AI systems that can understand and generate spoken language — enabling voice-based interactions with software. Voice AI powers smart speakers (Alexa, Siri, Google Assistant), AI phone receptionists, and voice-enabled customer service tools. For small businesses, voice AI can handle inbound calls, answer common questions, and route enquiries — 24 hours a day.
Workflow
A sequence of steps or tasks that make up a business process. In the context of AI and automation, a workflow is the series of actions that get triggered and executed automatically — for example, a new lead fills in a form, which triggers an AI to qualify them, send a welcome email, and add them to your CRM.
xAI
The AI company founded by Elon Musk in 2023, responsible for building Grok. xAI's stated mission is to understand the true nature of the universe through AI research. It's one of the newer major players in the AI space and is notable for its integration with the X (formerly Twitter) platform.
Yield (AI Output Quality)
In AI contexts, yield refers to the quality and usefulness of the outputs an AI produces relative to the inputs given. High-yield prompting means getting consistently valuable, accurate, and relevant responses. Improving your yield is largely about better prompts, clearer context, and choosing the right model for the task.
Zero-Shot Learning
The ability of an AI model to perform a task it hasn't been specifically trained on, using only a description or instruction. For example, asking ChatGPT to classify customer feedback into categories it's never seen before. Zero-shot capability is one of the things that makes modern LLMs so versatile for business use.
AI Washing
When a company exaggerates or falsely claims that its product uses AI to appear more innovative or attract investment. Similar to 'greenwashing' in sustainability. As AI becomes a buzzword, AI washing is increasingly common — it's worth asking vendors exactly how AI is used in their product, not just whether it is.
Algorithm
A set of step-by-step instructions a computer follows to complete a task or solve a problem. Think of it like a recipe — the computer follows each step in order to produce a result. Most AI tools are built on complex algorithms that process data and generate outputs.
Artificial Intelligence (AI)
Technology that enables computers to perform tasks that normally require human intelligence — things like understanding language, recognizing images, making decisions, and learning from experience. AI is an umbrella term that covers many different technologies and approaches.
Automation
Using technology to perform tasks with little or no human input. In a business context, automation means setting up systems that handle repetitive work automatically — like sending follow-up emails, updating spreadsheets, or routing customer enquiries — so your team can focus on higher-value work.
Agentic AI
AI that can take actions and make decisions autonomously to complete a goal — not just answer questions, but actually do things like browse the web, send emails, or interact with software on your behalf. Agentic AI is one of the fastest-growing areas of AI development.
AI-Native
A product, business, or workflow that is built from the ground up with AI at its core — rather than having AI bolted on as an afterthought. An AI-native business uses AI as a fundamental part of how it operates, not just as an occasional tool. Increasingly, being AI-native is a competitive advantage.
API (Application Programming Interface)
A way for two software applications to talk to each other. When we integrate an AI tool with your existing software, we're usually using APIs to connect them. You don't need to understand how APIs work — just know they're the plumbing that makes integrations possible.
ChatGPT
The AI assistant built by OpenAI and one of the most widely used AI tools in the world. ChatGPT can write, summarize, answer questions, draft emails, generate ideas, and much more — all through a simple chat interface. It runs on OpenAI's GPT models and is available free or via a paid plan. For most small businesses, it's the best starting point for exploring what AI can do.
Claude
An AI assistant built by Anthropic, widely regarded as one of the best alternatives to ChatGPT. Claude is known for nuanced writing, long-document analysis, and following complex instructions carefully. It's designed with a strong focus on safety and honesty. Available at claude.ai with a free tier and paid plans.
Bot
Short for 'robot' — a software program that performs automated tasks. In business, bots are commonly used for customer service (chatbots), data collection, and repetitive admin tasks. Not all bots use AI, but AI-powered bots are far more capable than simple rule-based ones.
Benchmark
A standardised test used to measure and compare the performance of AI models. When you see claims like 'our AI scores 90% on X benchmark', it means the model was tested against a standard set of problems. Benchmarks help compare different AI tools, though real-world performance can vary.
Black Box
A system where you can see the inputs and outputs but not what happens in between. Most AI models are black boxes — you give them a prompt and get a response, but the internal reasoning is not visible or easily explainable. This is why 'explainable AI' is an active area of research, particularly in regulated industries like finance and healthcare.
Chatbot
A software program designed to simulate conversation with humans, typically via text. Modern AI-powered chatbots (like those built on ChatGPT) can understand natural language, answer complex questions, and handle customer service tasks around the clock — far beyond the simple 'press 1 for billing' bots of the past.
Context Window
The amount of text an AI model can 'see' and consider at one time. Think of it like short-term memory — the larger the context window, the more of a conversation or document the AI can take into account when generating a response. Larger context windows generally produce more coherent, relevant outputs.
Copilot
An AI assistant that works alongside a human to help them do their job better — not replacing them, but augmenting their capabilities. Microsoft's Copilot (built into Office 365) is a well-known example. The term reflects the idea that the human is still in control; the AI is just helping navigate.
Data
Information in a form that computers can process. AI systems learn from data — the more relevant, high-quality data an AI is trained on, the better it tends to perform. In a business context, your data includes things like customer records, transaction history, emails, and documents.
Deepfake
AI-generated video, audio, or images that realistically depict someone saying or doing something they never actually said or did. Deepfakes are created using deep learning techniques and have become increasingly convincing. As a business owner, it's important to be aware of deepfakes — both as a potential security risk (e.g. fake audio of a CEO authorising a transfer) and as a reputational concern.
Deep Learning
A type of machine learning that uses neural networks with many layers to learn from large amounts of data. Deep learning powers many of the most impressive AI capabilities today — including image recognition, speech recognition, and large language models like ChatGPT.
Embedding
A way of representing text, images, or other data as numbers so that an AI can understand relationships between concepts. Embeddings allow AI to understand that 'dog' and 'puppy' are related, or that 'invoice' and 'bill' mean similar things — even without being explicitly told.
Emergent Behavior
Unexpected capabilities that appear in AI models as they grow larger or are trained on more data — abilities that weren't explicitly programmed or anticipated. For example, large language models spontaneously developed the ability to do basic arithmetic and translate languages without being specifically trained to do so. Emergent behavior is one of the reasons AI development can be unpredictable.
Fine-Tuning
The process of taking a pre-trained AI model and training it further on a specific dataset to make it better at a particular task. For example, fine-tuning a general language model on your company's documents and tone of voice so it writes in your style and understands your business context.
Frontier Model
The most advanced AI models available at any given time — the cutting edge of what's technically possible. GPT-4o, Claude 3.5 Sonnet, and Gemini Ultra are examples of frontier models. The term is used in AI policy and safety discussions to describe models powerful enough to potentially pose novel risks alongside their capabilities.
Foundation Model
A large AI model trained on a broad dataset that can be adapted for many different tasks. GPT-4, Claude, and Gemini are all foundation models. They're called 'foundation' models because they serve as the base that other, more specialized AI applications are built on top of.
Gemini
Google's flagship AI assistant and language model, available at gemini.google.com. Gemini integrates natively with Google Workspace — Gmail, Docs, Sheets, Drive — making it a strong choice for businesses already running on Google tools. It competes directly with ChatGPT and Claude and is available with a free tier and paid Google One AI Premium plan.
Grok
An AI assistant built by xAI, Elon Musk's AI company, and integrated into the X (formerly Twitter) platform. Grok is notable for having real-time access to information posted on X and for taking a less filtered approach to responses compared to some competitors. Available to X Premium subscribers and via the Grok standalone app.
Generative AI
AI that can create new content — text, images, audio, video, code — rather than just analysing or classifying existing content. ChatGPT generating a business proposal, DALL-E creating an image from a description, and GitHub Copilot writing code are all examples of generative AI in action.
GPT (Generative Pre-trained Transformer)
The type of AI model that powers ChatGPT, developed by OpenAI. GPT models are trained on vast amounts of text data and can generate human-like text, answer questions, summarize documents, write code, and much more. The 'transformer' part refers to the underlying architecture that makes modern AI so powerful.
Guardrails
Rules, filters, and safety mechanisms built into AI systems to prevent them from producing harmful, offensive, or inappropriate outputs. Guardrails are why AI assistants refuse certain requests. When building custom AI tools for businesses, we also add business-specific guardrails — for example, preventing a customer service bot from making promises the business can't keep.
Hallucination
When an AI confidently produces information that is factually incorrect or completely made up. This is one of the most important limitations of current AI tools to understand. AI doesn't 'know' things the way humans do — it predicts likely text, which can sometimes result in plausible-sounding but wrong answers. Always verify important facts.
Human-in-the-Loop
A system design where humans are involved in reviewing, approving, or correcting AI outputs before they're acted on. For most business applications, keeping a human in the loop is best practice — especially for anything customer-facing, financial, or legally sensitive.
Hyperparameter
Settings that control how an AI model is trained — like how fast it learns, how many training rounds it goes through, or how large its layers are. Unlike regular parameters (which the model learns automatically), hyperparameters are set by the people building the model. Getting them right is part of the craft of AI development.
Inference
The process of using a trained AI model to generate outputs from new inputs. When you type a question into ChatGPT and it responds, that's inference — the model is applying what it learned during training to your specific input. Training happens once; inference happens every time you use the tool.
Integration
Connecting two or more software systems so they can share data and work together. In AI, integration typically means connecting an AI tool to your existing business software — like linking a chatbot to your CRM, or connecting an automation tool to your booking system.
Jailbreak
An attempt to bypass the safety guidelines or restrictions built into an AI model — usually by crafting clever prompts that trick the AI into ignoring its rules. AI companies work continuously to prevent jailbreaks. As a business owner, it's worth knowing this exists so you understand why AI tools sometimes refuse certain requests — those guardrails are there for good reason.
Knowledge Base
A structured collection of information that an AI can draw on to answer questions. When we build a custom AI assistant for your business, we often create a knowledge base containing your FAQs, product details, policies, and procedures — so the AI gives accurate, business-specific answers rather than generic ones.
Large Language Model (LLM)
An AI model trained on enormous amounts of text data that can understand and generate human language. ChatGPT, Claude, and Gemini are all LLMs. They're called 'large' because of the sheer scale of data they're trained on and the number of parameters (internal settings) they contain — often in the billions.
Llama
Meta's family of open-source large language models, released for anyone to download, run, and build on. Unlike ChatGPT or Claude, Llama models can be run locally on your own hardware without sending data to a third-party server — making them popular for privacy-sensitive applications and businesses that want more control over their AI stack.
Machine Learning (ML)
A branch of AI where systems learn from data and improve their performance over time without being explicitly programmed for every scenario. Instead of following rigid rules, machine learning models identify patterns in data and use those patterns to make predictions or decisions.
Multimodal AI
AI that can process and generate multiple types of content — text, images, audio, and video — rather than just one. GPT-4o is multimodal: you can show it a photo and ask a question about it, or have it describe an image. Multimodal AI opens up a much wider range of business applications than text-only models.
Model
In AI, a model is the trained system that takes inputs and produces outputs. When people refer to 'the model', they mean the AI itself — the mathematical structure that has been trained on data. Different models have different strengths, weaknesses, and use cases.
Natural Language Processing (NLP)
The branch of AI focused on enabling computers to understand, interpret, and generate human language. NLP powers chatbots, voice assistants, translation tools, sentiment analysis, and much more. When an AI understands what you're asking in plain English, that's NLP at work.
Neural Network
A type of AI system loosely inspired by the structure of the human brain, made up of layers of interconnected nodes that process information. Neural networks are the foundation of most modern AI — including image recognition, speech recognition, and large language models.
No-Code / Low-Code
Tools and platforms that allow people to build software applications and automations without writing traditional code. Many AI tools are now available in no-code or low-code formats, making them accessible to business owners without technical backgrounds. Zapier and Make are popular examples.
OpenAI
The company behind ChatGPT, GPT-4, and DALL-E. OpenAI is one of the most influential AI research organizations in the world. Founded in 2015, it has become the most recognized name in consumer AI and provides the underlying technology for many third-party AI tools and applications.
Overfitting
When an AI model learns its training data too well — including the noise and quirks — and as a result performs poorly on new, real-world data. Think of it like a student who memorises past exam papers word-for-word but can't answer a question that's phrased slightly differently. Overfitting is a common challenge in building reliable AI systems.
Parameters
The internal settings of an AI model that are adjusted during training to improve performance. A model with more parameters is generally more capable but also more expensive to run. GPT-4 is estimated to have hundreds of billions of parameters — which is part of why it's so capable.
Prompt
The input you give to an AI model — the question, instruction, or context that tells the AI what you want it to do. Writing effective prompts (prompt engineering) is a skill that significantly affects the quality of AI outputs. A well-crafted prompt can be the difference between a mediocre and an excellent result.
Prompt Engineering
The practice of designing and refining prompts to get better outputs from AI models. It involves understanding how AI models interpret instructions and structuring your inputs to guide the model toward the response you want. Good prompt engineering is one of the most practical AI skills a business owner can develop.
Prompt Injection
A type of attack where a malicious user crafts inputs designed to override an AI system's instructions and make it behave in unintended ways. For example, a user might try to trick a customer service chatbot into ignoring its rules by embedding hidden instructions in their message. It's a real security concern for businesses deploying AI-facing tools — and something we account for when building AI systems for clients.
Query
A question or request submitted to an AI system, search engine, or database. In everyday AI use, a query is essentially what you type into ChatGPT or a search bar. In more technical contexts, queries are used to retrieve specific data from databases. The quality of your query directly affects the quality of the answer you get back.
RAG (Retrieval-Augmented Generation)
A technique that combines an AI language model with a search system, allowing the AI to retrieve relevant information from a specific knowledge base before generating a response. RAG is how you build AI tools that can answer questions about your specific business — your products, policies, FAQs — rather than just general knowledge.
Sentiment Analysis
An AI technique that identifies and categorises the emotional tone of text — positive, negative, or neutral. Businesses use sentiment analysis to monitor customer reviews, social media mentions, and support tickets to understand how customers feel about their products or services at scale.
Shadow AI
The use of AI tools by employees without the knowledge or approval of their organization's IT or management team. Just as 'shadow IT' described unauthorised software use, shadow AI is a growing concern — staff may be pasting sensitive client data into ChatGPT or using unapproved AI tools, creating data privacy and security risks. Having a clear AI policy in your business is increasingly important.
System Prompt
A set of instructions given to an AI model before a conversation begins, used to define its persona, rules, and behavior. When we build a custom AI chatbot for a business, the system prompt is where we tell it who it is, what it can and can't do, and how it should respond to customers.
Token
The basic unit of text that AI language models process. A token is roughly equivalent to a word or part of a word — 'hamburger' might be split into two tokens, while 'the' is one. AI models have token limits for inputs and outputs, and usage is often billed per token. Understanding tokens helps you use AI tools more efficiently.
Training
The process of teaching an AI model by exposing it to large amounts of data and adjusting its parameters to improve performance. Training a large AI model requires enormous computing power and can take weeks or months. Most businesses use pre-trained models rather than training their own from scratch.
Transformer
The underlying architecture that powers most modern AI language models, including GPT, Claude, and Gemini. Introduced by Google in 2017, the transformer architecture revolutionised AI by enabling models to process and understand context across long sequences of text far more effectively than previous approaches.
Use Case
A specific, practical application of a technology to solve a real problem. In AI, a use case might be 'using AI to automatically respond to common customer enquiries' or 'using AI to summarize meeting notes'. Identifying the right use cases for your business is the first step in any successful AI implementation.
Vector Database
A type of database designed to store and search data in the format that AI models use (vectors/embeddings). Vector databases are a key component of RAG systems — they allow AI tools to quickly find the most relevant information from a large knowledge base when answering a question.
Vibe Coding
A term coined in 2025 describing the practice of building software by describing what you want in plain English to an AI — and letting the AI write the code for you. Instead of learning to code, you 'vibe' with the AI: you describe the idea, review what it builds, give feedback, and iterate. Tools like Cursor, GitHub Copilot, and platforms like the one this website was built on make vibe coding accessible to non-developers. It's one of the most significant shifts in how software gets made.
Voice AI
AI systems that can understand and generate spoken language — enabling voice-based interactions with software. Voice AI powers smart speakers (Alexa, Siri, Google Assistant), AI phone receptionists, and voice-enabled customer service tools. For small businesses, voice AI can handle inbound calls, answer common questions, and route enquiries — 24 hours a day.
Workflow
A sequence of steps or tasks that make up a business process. In the context of AI and automation, a workflow is the series of actions that get triggered and executed automatically — for example, a new lead fills in a form, which triggers an AI to qualify them, send a welcome email, and add them to your CRM.
xAI
The AI company founded by Elon Musk in 2023, responsible for building Grok. xAI's stated mission is to understand the true nature of the universe through AI research. It's one of the newer major players in the AI space and is notable for its integration with the X (formerly Twitter) platform.
Yield (AI Output Quality)
In AI contexts, yield refers to the quality and usefulness of the outputs an AI produces relative to the inputs given. High-yield prompting means getting consistently valuable, accurate, and relevant responses. Improving your yield is largely about better prompts, clearer context, and choosing the right model for the task.
Zero-Shot Learning
The ability of an AI model to perform a task it hasn't been specifically trained on, using only a description or instruction. For example, asking ChatGPT to classify customer feedback into categories it's never seen before. Zero-shot capability is one of the things that makes modern LLMs so versatile for business use.
AI Washing
When a company exaggerates or falsely claims that its product uses AI to appear more innovative or attract investment. Similar to 'greenwashing' in sustainability. As AI becomes a buzzword, AI washing is increasingly common — it's worth asking vendors exactly how AI is used in their product, not just whether it is.
Algorithm
A set of step-by-step instructions a computer follows to complete a task or solve a problem. Think of it like a recipe — the computer follows each step in order to produce a result. Most AI tools are built on complex algorithms that process data and generate outputs.
Artificial Intelligence (AI)
Technology that enables computers to perform tasks that normally require human intelligence — things like understanding language, recognizing images, making decisions, and learning from experience. AI is an umbrella term that covers many different technologies and approaches.
Automation
Using technology to perform tasks with little or no human input. In a business context, automation means setting up systems that handle repetitive work automatically — like sending follow-up emails, updating spreadsheets, or routing customer enquiries — so your team can focus on higher-value work.
Agentic AI
AI that can take actions and make decisions autonomously to complete a goal — not just answer questions, but actually do things like browse the web, send emails, or interact with software on your behalf. Agentic AI is one of the fastest-growing areas of AI development.
AI-Native
A product, business, or workflow that is built from the ground up with AI at its core — rather than having AI bolted on as an afterthought. An AI-native business uses AI as a fundamental part of how it operates, not just as an occasional tool. Increasingly, being AI-native is a competitive advantage.
API (Application Programming Interface)
A way for two software applications to talk to each other. When we integrate an AI tool with your existing software, we're usually using APIs to connect them. You don't need to understand how APIs work — just know they're the plumbing that makes integrations possible.
ChatGPT
The AI assistant built by OpenAI and one of the most widely used AI tools in the world. ChatGPT can write, summarize, answer questions, draft emails, generate ideas, and much more — all through a simple chat interface. It runs on OpenAI's GPT models and is available free or via a paid plan. For most small businesses, it's the best starting point for exploring what AI can do.
Claude
An AI assistant built by Anthropic, widely regarded as one of the best alternatives to ChatGPT. Claude is known for nuanced writing, long-document analysis, and following complex instructions carefully. It's designed with a strong focus on safety and honesty. Available at claude.ai with a free tier and paid plans.
Bot
Short for 'robot' — a software program that performs automated tasks. In business, bots are commonly used for customer service (chatbots), data collection, and repetitive admin tasks. Not all bots use AI, but AI-powered bots are far more capable than simple rule-based ones.
Benchmark
A standardised test used to measure and compare the performance of AI models. When you see claims like 'our AI scores 90% on X benchmark', it means the model was tested against a standard set of problems. Benchmarks help compare different AI tools, though real-world performance can vary.
Black Box
A system where you can see the inputs and outputs but not what happens in between. Most AI models are black boxes — you give them a prompt and get a response, but the internal reasoning is not visible or easily explainable. This is why 'explainable AI' is an active area of research, particularly in regulated industries like finance and healthcare.
Chatbot
A software program designed to simulate conversation with humans, typically via text. Modern AI-powered chatbots (like those built on ChatGPT) can understand natural language, answer complex questions, and handle customer service tasks around the clock — far beyond the simple 'press 1 for billing' bots of the past.
Context Window
The amount of text an AI model can 'see' and consider at one time. Think of it like short-term memory — the larger the context window, the more of a conversation or document the AI can take into account when generating a response. Larger context windows generally produce more coherent, relevant outputs.
Copilot
An AI assistant that works alongside a human to help them do their job better — not replacing them, but augmenting their capabilities. Microsoft's Copilot (built into Office 365) is a well-known example. The term reflects the idea that the human is still in control; the AI is just helping navigate.
Data
Information in a form that computers can process. AI systems learn from data — the more relevant, high-quality data an AI is trained on, the better it tends to perform. In a business context, your data includes things like customer records, transaction history, emails, and documents.
Deepfake
AI-generated video, audio, or images that realistically depict someone saying or doing something they never actually said or did. Deepfakes are created using deep learning techniques and have become increasingly convincing. As a business owner, it's important to be aware of deepfakes — both as a potential security risk (e.g. fake audio of a CEO authorising a transfer) and as a reputational concern.
Deep Learning
A type of machine learning that uses neural networks with many layers to learn from large amounts of data. Deep learning powers many of the most impressive AI capabilities today — including image recognition, speech recognition, and large language models like ChatGPT.
Embedding
A way of representing text, images, or other data as numbers so that an AI can understand relationships between concepts. Embeddings allow AI to understand that 'dog' and 'puppy' are related, or that 'invoice' and 'bill' mean similar things — even without being explicitly told.
Emergent Behavior
Unexpected capabilities that appear in AI models as they grow larger or are trained on more data — abilities that weren't explicitly programmed or anticipated. For example, large language models spontaneously developed the ability to do basic arithmetic and translate languages without being specifically trained to do so. Emergent behavior is one of the reasons AI development can be unpredictable.
Fine-Tuning
The process of taking a pre-trained AI model and training it further on a specific dataset to make it better at a particular task. For example, fine-tuning a general language model on your company's documents and tone of voice so it writes in your style and understands your business context.
Frontier Model
The most advanced AI models available at any given time — the cutting edge of what's technically possible. GPT-4o, Claude 3.5 Sonnet, and Gemini Ultra are examples of frontier models. The term is used in AI policy and safety discussions to describe models powerful enough to potentially pose novel risks alongside their capabilities.
Foundation Model
A large AI model trained on a broad dataset that can be adapted for many different tasks. GPT-4, Claude, and Gemini are all foundation models. They're called 'foundation' models because they serve as the base that other, more specialized AI applications are built on top of.
Gemini
Google's flagship AI assistant and language model, available at gemini.google.com. Gemini integrates natively with Google Workspace — Gmail, Docs, Sheets, Drive — making it a strong choice for businesses already running on Google tools. It competes directly with ChatGPT and Claude and is available with a free tier and paid Google One AI Premium plan.
Grok
An AI assistant built by xAI, Elon Musk's AI company, and integrated into the X (formerly Twitter) platform. Grok is notable for having real-time access to information posted on X and for taking a less filtered approach to responses compared to some competitors. Available to X Premium subscribers and via the Grok standalone app.
Generative AI
AI that can create new content — text, images, audio, video, code — rather than just analysing or classifying existing content. ChatGPT generating a business proposal, DALL-E creating an image from a description, and GitHub Copilot writing code are all examples of generative AI in action.
GPT (Generative Pre-trained Transformer)
The type of AI model that powers ChatGPT, developed by OpenAI. GPT models are trained on vast amounts of text data and can generate human-like text, answer questions, summarize documents, write code, and much more. The 'transformer' part refers to the underlying architecture that makes modern AI so powerful.
Guardrails
Rules, filters, and safety mechanisms built into AI systems to prevent them from producing harmful, offensive, or inappropriate outputs. Guardrails are why AI assistants refuse certain requests. When building custom AI tools for businesses, we also add business-specific guardrails — for example, preventing a customer service bot from making promises the business can't keep.
Hallucination
When an AI confidently produces information that is factually incorrect or completely made up. This is one of the most important limitations of current AI tools to understand. AI doesn't 'know' things the way humans do — it predicts likely text, which can sometimes result in plausible-sounding but wrong answers. Always verify important facts.
Human-in-the-Loop
A system design where humans are involved in reviewing, approving, or correcting AI outputs before they're acted on. For most business applications, keeping a human in the loop is best practice — especially for anything customer-facing, financial, or legally sensitive.
Hyperparameter
Settings that control how an AI model is trained — like how fast it learns, how many training rounds it goes through, or how large its layers are. Unlike regular parameters (which the model learns automatically), hyperparameters are set by the people building the model. Getting them right is part of the craft of AI development.
Inference
The process of using a trained AI model to generate outputs from new inputs. When you type a question into ChatGPT and it responds, that's inference — the model is applying what it learned during training to your specific input. Training happens once; inference happens every time you use the tool.
Integration
Connecting two or more software systems so they can share data and work together. In AI, integration typically means connecting an AI tool to your existing business software — like linking a chatbot to your CRM, or connecting an automation tool to your booking system.
Jailbreak
An attempt to bypass the safety guidelines or restrictions built into an AI model — usually by crafting clever prompts that trick the AI into ignoring its rules. AI companies work continuously to prevent jailbreaks. As a business owner, it's worth knowing this exists so you understand why AI tools sometimes refuse certain requests — those guardrails are there for good reason.
Knowledge Base
A structured collection of information that an AI can draw on to answer questions. When we build a custom AI assistant for your business, we often create a knowledge base containing your FAQs, product details, policies, and procedures — so the AI gives accurate, business-specific answers rather than generic ones.
Large Language Model (LLM)
An AI model trained on enormous amounts of text data that can understand and generate human language. ChatGPT, Claude, and Gemini are all LLMs. They're called 'large' because of the sheer scale of data they're trained on and the number of parameters (internal settings) they contain — often in the billions.
Llama
Meta's family of open-source large language models, released for anyone to download, run, and build on. Unlike ChatGPT or Claude, Llama models can be run locally on your own hardware without sending data to a third-party server — making them popular for privacy-sensitive applications and businesses that want more control over their AI stack.
Machine Learning (ML)
A branch of AI where systems learn from data and improve their performance over time without being explicitly programmed for every scenario. Instead of following rigid rules, machine learning models identify patterns in data and use those patterns to make predictions or decisions.
Multimodal AI
AI that can process and generate multiple types of content — text, images, audio, and video — rather than just one. GPT-4o is multimodal: you can show it a photo and ask a question about it, or have it describe an image. Multimodal AI opens up a much wider range of business applications than text-only models.
Model
In AI, a model is the trained system that takes inputs and produces outputs. When people refer to 'the model', they mean the AI itself — the mathematical structure that has been trained on data. Different models have different strengths, weaknesses, and use cases.
Natural Language Processing (NLP)
The branch of AI focused on enabling computers to understand, interpret, and generate human language. NLP powers chatbots, voice assistants, translation tools, sentiment analysis, and much more. When an AI understands what you're asking in plain English, that's NLP at work.
Neural Network
A type of AI system loosely inspired by the structure of the human brain, made up of layers of interconnected nodes that process information. Neural networks are the foundation of most modern AI — including image recognition, speech recognition, and large language models.
No-Code / Low-Code
Tools and platforms that allow people to build software applications and automations without writing traditional code. Many AI tools are now available in no-code or low-code formats, making them accessible to business owners without technical backgrounds. Zapier and Make are popular examples.
OpenAI
The company behind ChatGPT, GPT-4, and DALL-E. OpenAI is one of the most influential AI research organizations in the world. Founded in 2015, it has become the most recognized name in consumer AI and provides the underlying technology for many third-party AI tools and applications.
Overfitting
When an AI model learns its training data too well — including the noise and quirks — and as a result performs poorly on new, real-world data. Think of it like a student who memorises past exam papers word-for-word but can't answer a question that's phrased slightly differently. Overfitting is a common challenge in building reliable AI systems.
Parameters
The internal settings of an AI model that are adjusted during training to improve performance. A model with more parameters is generally more capable but also more expensive to run. GPT-4 is estimated to have hundreds of billions of parameters — which is part of why it's so capable.
Prompt
The input you give to an AI model — the question, instruction, or context that tells the AI what you want it to do. Writing effective prompts (prompt engineering) is a skill that significantly affects the quality of AI outputs. A well-crafted prompt can be the difference between a mediocre and an excellent result.
Prompt Engineering
The practice of designing and refining prompts to get better outputs from AI models. It involves understanding how AI models interpret instructions and structuring your inputs to guide the model toward the response you want. Good prompt engineering is one of the most practical AI skills a business owner can develop.
Prompt Injection
A type of attack where a malicious user crafts inputs designed to override an AI system's instructions and make it behave in unintended ways. For example, a user might try to trick a customer service chatbot into ignoring its rules by embedding hidden instructions in their message. It's a real security concern for businesses deploying AI-facing tools — and something we account for when building AI systems for clients.
Query
A question or request submitted to an AI system, search engine, or database. In everyday AI use, a query is essentially what you type into ChatGPT or a search bar. In more technical contexts, queries are used to retrieve specific data from databases. The quality of your query directly affects the quality of the answer you get back.
RAG (Retrieval-Augmented Generation)
A technique that combines an AI language model with a search system, allowing the AI to retrieve relevant information from a specific knowledge base before generating a response. RAG is how you build AI tools that can answer questions about your specific business — your products, policies, FAQs — rather than just general knowledge.
Sentiment Analysis
An AI technique that identifies and categorises the emotional tone of text — positive, negative, or neutral. Businesses use sentiment analysis to monitor customer reviews, social media mentions, and support tickets to understand how customers feel about their products or services at scale.
Shadow AI
The use of AI tools by employees without the knowledge or approval of their organization's IT or management team. Just as 'shadow IT' described unauthorised software use, shadow AI is a growing concern — staff may be pasting sensitive client data into ChatGPT or using unapproved AI tools, creating data privacy and security risks. Having a clear AI policy in your business is increasingly important.
System Prompt
A set of instructions given to an AI model before a conversation begins, used to define its persona, rules, and behavior. When we build a custom AI chatbot for a business, the system prompt is where we tell it who it is, what it can and can't do, and how it should respond to customers.
Token
The basic unit of text that AI language models process. A token is roughly equivalent to a word or part of a word — 'hamburger' might be split into two tokens, while 'the' is one. AI models have token limits for inputs and outputs, and usage is often billed per token. Understanding tokens helps you use AI tools more efficiently.
Training
The process of teaching an AI model by exposing it to large amounts of data and adjusting its parameters to improve performance. Training a large AI model requires enormous computing power and can take weeks or months. Most businesses use pre-trained models rather than training their own from scratch.
Transformer
The underlying architecture that powers most modern AI language models, including GPT, Claude, and Gemini. Introduced by Google in 2017, the transformer architecture revolutionised AI by enabling models to process and understand context across long sequences of text far more effectively than previous approaches.
Use Case
A specific, practical application of a technology to solve a real problem. In AI, a use case might be 'using AI to automatically respond to common customer enquiries' or 'using AI to summarize meeting notes'. Identifying the right use cases for your business is the first step in any successful AI implementation.
Vector Database
A type of database designed to store and search data in the format that AI models use (vectors/embeddings). Vector databases are a key component of RAG systems — they allow AI tools to quickly find the most relevant information from a large knowledge base when answering a question.
Vibe Coding
A term coined in 2025 describing the practice of building software by describing what you want in plain English to an AI — and letting the AI write the code for you. Instead of learning to code, you 'vibe' with the AI: you describe the idea, review what it builds, give feedback, and iterate. Tools like Cursor, GitHub Copilot, and platforms like the one this website was built on make vibe coding accessible to non-developers. It's one of the most significant shifts in how software gets made.
Voice AI
AI systems that can understand and generate spoken language — enabling voice-based interactions with software. Voice AI powers smart speakers (Alexa, Siri, Google Assistant), AI phone receptionists, and voice-enabled customer service tools. For small businesses, voice AI can handle inbound calls, answer common questions, and route enquiries — 24 hours a day.
Workflow
A sequence of steps or tasks that make up a business process. In the context of AI and automation, a workflow is the series of actions that get triggered and executed automatically — for example, a new lead fills in a form, which triggers an AI to qualify them, send a welcome email, and add them to your CRM.
xAI
The AI company founded by Elon Musk in 2023, responsible for building Grok. xAI's stated mission is to understand the true nature of the universe through AI research. It's one of the newer major players in the AI space and is notable for its integration with the X (formerly Twitter) platform.
Yield (AI Output Quality)
In AI contexts, yield refers to the quality and usefulness of the outputs an AI produces relative to the inputs given. High-yield prompting means getting consistently valuable, accurate, and relevant responses. Improving your yield is largely about better prompts, clearer context, and choosing the right model for the task.
Zero-Shot Learning
The ability of an AI model to perform a task it hasn't been specifically trained on, using only a description or instruction. For example, asking ChatGPT to classify customer feedback into categories it's never seen before. Zero-shot capability is one of the things that makes modern LLMs so versatile for business use.
AI Washing
When a company exaggerates or falsely claims that its product uses AI to appear more innovative or attract investment. Similar to 'greenwashing' in sustainability. As AI becomes a buzzword, AI washing is increasingly common — it's worth asking vendors exactly how AI is used in their product, not just whether it is.
Algorithm
A set of step-by-step instructions a computer follows to complete a task or solve a problem. Think of it like a recipe — the computer follows each step in order to produce a result. Most AI tools are built on complex algorithms that process data and generate outputs.
Artificial Intelligence (AI)
Technology that enables computers to perform tasks that normally require human intelligence — things like understanding language, recognizing images, making decisions, and learning from experience. AI is an umbrella term that covers many different technologies and approaches.
Automation
Using technology to perform tasks with little or no human input. In a business context, automation means setting up systems that handle repetitive work automatically — like sending follow-up emails, updating spreadsheets, or routing customer enquiries — so your team can focus on higher-value work.
Agentic AI
AI that can take actions and make decisions autonomously to complete a goal — not just answer questions, but actually do things like browse the web, send emails, or interact with software on your behalf. Agentic AI is one of the fastest-growing areas of AI development.
AI-Native
A product, business, or workflow that is built from the ground up with AI at its core — rather than having AI bolted on as an afterthought. An AI-native business uses AI as a fundamental part of how it operates, not just as an occasional tool. Increasingly, being AI-native is a competitive advantage.
API (Application Programming Interface)
A way for two software applications to talk to each other. When we integrate an AI tool with your existing software, we're usually using APIs to connect them. You don't need to understand how APIs work — just know they're the plumbing that makes integrations possible.
ChatGPT
The AI assistant built by OpenAI and one of the most widely used AI tools in the world. ChatGPT can write, summarize, answer questions, draft emails, generate ideas, and much more — all through a simple chat interface. It runs on OpenAI's GPT models and is available free or via a paid plan. For most small businesses, it's the best starting point for exploring what AI can do.
Claude
An AI assistant built by Anthropic, widely regarded as one of the best alternatives to ChatGPT. Claude is known for nuanced writing, long-document analysis, and following complex instructions carefully. It's designed with a strong focus on safety and honesty. Available at claude.ai with a free tier and paid plans.
Bot
Short for 'robot' — a software program that performs automated tasks. In business, bots are commonly used for customer service (chatbots), data collection, and repetitive admin tasks. Not all bots use AI, but AI-powered bots are far more capable than simple rule-based ones.
Benchmark
A standardised test used to measure and compare the performance of AI models. When you see claims like 'our AI scores 90% on X benchmark', it means the model was tested against a standard set of problems. Benchmarks help compare different AI tools, though real-world performance can vary.
Black Box
A system where you can see the inputs and outputs but not what happens in between. Most AI models are black boxes — you give them a prompt and get a response, but the internal reasoning is not visible or easily explainable. This is why 'explainable AI' is an active area of research, particularly in regulated industries like finance and healthcare.
Chatbot
A software program designed to simulate conversation with humans, typically via text. Modern AI-powered chatbots (like those built on ChatGPT) can understand natural language, answer complex questions, and handle customer service tasks around the clock — far beyond the simple 'press 1 for billing' bots of the past.
Context Window
The amount of text an AI model can 'see' and consider at one time. Think of it like short-term memory — the larger the context window, the more of a conversation or document the AI can take into account when generating a response. Larger context windows generally produce more coherent, relevant outputs.
Copilot
An AI assistant that works alongside a human to help them do their job better — not replacing them, but augmenting their capabilities. Microsoft's Copilot (built into Office 365) is a well-known example. The term reflects the idea that the human is still in control; the AI is just helping navigate.
Data
Information in a form that computers can process. AI systems learn from data — the more relevant, high-quality data an AI is trained on, the better it tends to perform. In a business context, your data includes things like customer records, transaction history, emails, and documents.
Deepfake
AI-generated video, audio, or images that realistically depict someone saying or doing something they never actually said or did. Deepfakes are created using deep learning techniques and have become increasingly convincing. As a business owner, it's important to be aware of deepfakes — both as a potential security risk (e.g. fake audio of a CEO authorising a transfer) and as a reputational concern.
Deep Learning
A type of machine learning that uses neural networks with many layers to learn from large amounts of data. Deep learning powers many of the most impressive AI capabilities today — including image recognition, speech recognition, and large language models like ChatGPT.
Embedding
A way of representing text, images, or other data as numbers so that an AI can understand relationships between concepts. Embeddings allow AI to understand that 'dog' and 'puppy' are related, or that 'invoice' and 'bill' mean similar things — even without being explicitly told.
Emergent Behavior
Unexpected capabilities that appear in AI models as they grow larger or are trained on more data — abilities that weren't explicitly programmed or anticipated. For example, large language models spontaneously developed the ability to do basic arithmetic and translate languages without being specifically trained to do so. Emergent behavior is one of the reasons AI development can be unpredictable.
Fine-Tuning
The process of taking a pre-trained AI model and training it further on a specific dataset to make it better at a particular task. For example, fine-tuning a general language model on your company's documents and tone of voice so it writes in your style and understands your business context.
Frontier Model
The most advanced AI models available at any given time — the cutting edge of what's technically possible. GPT-4o, Claude 3.5 Sonnet, and Gemini Ultra are examples of frontier models. The term is used in AI policy and safety discussions to describe models powerful enough to potentially pose novel risks alongside their capabilities.
Foundation Model
A large AI model trained on a broad dataset that can be adapted for many different tasks. GPT-4, Claude, and Gemini are all foundation models. They're called 'foundation' models because they serve as the base that other, more specialized AI applications are built on top of.
Gemini
Google's flagship AI assistant and language model, available at gemini.google.com. Gemini integrates natively with Google Workspace — Gmail, Docs, Sheets, Drive — making it a strong choice for businesses already running on Google tools. It competes directly with ChatGPT and Claude and is available with a free tier and paid Google One AI Premium plan.
Grok
An AI assistant built by xAI, Elon Musk's AI company, and integrated into the X (formerly Twitter) platform. Grok is notable for having real-time access to information posted on X and for taking a less filtered approach to responses compared to some competitors. Available to X Premium subscribers and via the Grok standalone app.
Generative AI
AI that can create new content — text, images, audio, video, code — rather than just analysing or classifying existing content. ChatGPT generating a business proposal, DALL-E creating an image from a description, and GitHub Copilot writing code are all examples of generative AI in action.
GPT (Generative Pre-trained Transformer)
The type of AI model that powers ChatGPT, developed by OpenAI. GPT models are trained on vast amounts of text data and can generate human-like text, answer questions, summarize documents, write code, and much more. The 'transformer' part refers to the underlying architecture that makes modern AI so powerful.
Guardrails
Rules, filters, and safety mechanisms built into AI systems to prevent them from producing harmful, offensive, or inappropriate outputs. Guardrails are why AI assistants refuse certain requests. When building custom AI tools for businesses, we also add business-specific guardrails — for example, preventing a customer service bot from making promises the business can't keep.
Hallucination
When an AI confidently produces information that is factually incorrect or completely made up. This is one of the most important limitations of current AI tools to understand. AI doesn't 'know' things the way humans do — it predicts likely text, which can sometimes result in plausible-sounding but wrong answers. Always verify important facts.
Human-in-the-Loop
A system design where humans are involved in reviewing, approving, or correcting AI outputs before they're acted on. For most business applications, keeping a human in the loop is best practice — especially for anything customer-facing, financial, or legally sensitive.
Hyperparameter
Settings that control how an AI model is trained — like how fast it learns, how many training rounds it goes through, or how large its layers are. Unlike regular parameters (which the model learns automatically), hyperparameters are set by the people building the model. Getting them right is part of the craft of AI development.
Inference
The process of using a trained AI model to generate outputs from new inputs. When you type a question into ChatGPT and it responds, that's inference — the model is applying what it learned during training to your specific input. Training happens once; inference happens every time you use the tool.
Integration
Connecting two or more software systems so they can share data and work together. In AI, integration typically means connecting an AI tool to your existing business software — like linking a chatbot to your CRM, or connecting an automation tool to your booking system.
Jailbreak
An attempt to bypass the safety guidelines or restrictions built into an AI model — usually by crafting clever prompts that trick the AI into ignoring its rules. AI companies work continuously to prevent jailbreaks. As a business owner, it's worth knowing this exists so you understand why AI tools sometimes refuse certain requests — those guardrails are there for good reason.
Knowledge Base
A structured collection of information that an AI can draw on to answer questions. When we build a custom AI assistant for your business, we often create a knowledge base containing your FAQs, product details, policies, and procedures — so the AI gives accurate, business-specific answers rather than generic ones.
Large Language Model (LLM)
An AI model trained on enormous amounts of text data that can understand and generate human language. ChatGPT, Claude, and Gemini are all LLMs. They're called 'large' because of the sheer scale of data they're trained on and the number of parameters (internal settings) they contain — often in the billions.
Llama
Meta's family of open-source large language models, released for anyone to download, run, and build on. Unlike ChatGPT or Claude, Llama models can be run locally on your own hardware without sending data to a third-party server — making them popular for privacy-sensitive applications and businesses that want more control over their AI stack.
Machine Learning (ML)
A branch of AI where systems learn from data and improve their performance over time without being explicitly programmed for every scenario. Instead of following rigid rules, machine learning models identify patterns in data and use those patterns to make predictions or decisions.
Multimodal AI
AI that can process and generate multiple types of content — text, images, audio, and video — rather than just one. GPT-4o is multimodal: you can show it a photo and ask a question about it, or have it describe an image. Multimodal AI opens up a much wider range of business applications than text-only models.
Model
In AI, a model is the trained system that takes inputs and produces outputs. When people refer to 'the model', they mean the AI itself — the mathematical structure that has been trained on data. Different models have different strengths, weaknesses, and use cases.
Natural Language Processing (NLP)
The branch of AI focused on enabling computers to understand, interpret, and generate human language. NLP powers chatbots, voice assistants, translation tools, sentiment analysis, and much more. When an AI understands what you're asking in plain English, that's NLP at work.
Neural Network
A type of AI system loosely inspired by the structure of the human brain, made up of layers of interconnected nodes that process information. Neural networks are the foundation of most modern AI — including image recognition, speech recognition, and large language models.
No-Code / Low-Code
Tools and platforms that allow people to build software applications and automations without writing traditional code. Many AI tools are now available in no-code or low-code formats, making them accessible to business owners without technical backgrounds. Zapier and Make are popular examples.
OpenAI
The company behind ChatGPT, GPT-4, and DALL-E. OpenAI is one of the most influential AI research organizations in the world. Founded in 2015, it has become the most recognized name in consumer AI and provides the underlying technology for many third-party AI tools and applications.
Overfitting
When an AI model learns its training data too well — including the noise and quirks — and as a result performs poorly on new, real-world data. Think of it like a student who memorises past exam papers word-for-word but can't answer a question that's phrased slightly differently. Overfitting is a common challenge in building reliable AI systems.
Parameters
The internal settings of an AI model that are adjusted during training to improve performance. A model with more parameters is generally more capable but also more expensive to run. GPT-4 is estimated to have hundreds of billions of parameters — which is part of why it's so capable.
Prompt
The input you give to an AI model — the question, instruction, or context that tells the AI what you want it to do. Writing effective prompts (prompt engineering) is a skill that significantly affects the quality of AI outputs. A well-crafted prompt can be the difference between a mediocre and an excellent result.
Prompt Engineering
The practice of designing and refining prompts to get better outputs from AI models. It involves understanding how AI models interpret instructions and structuring your inputs to guide the model toward the response you want. Good prompt engineering is one of the most practical AI skills a business owner can develop.
Prompt Injection
A type of attack where a malicious user crafts inputs designed to override an AI system's instructions and make it behave in unintended ways. For example, a user might try to trick a customer service chatbot into ignoring its rules by embedding hidden instructions in their message. It's a real security concern for businesses deploying AI-facing tools — and something we account for when building AI systems for clients.
Query
A question or request submitted to an AI system, search engine, or database. In everyday AI use, a query is essentially what you type into ChatGPT or a search bar. In more technical contexts, queries are used to retrieve specific data from databases. The quality of your query directly affects the quality of the answer you get back.
RAG (Retrieval-Augmented Generation)
A technique that combines an AI language model with a search system, allowing the AI to retrieve relevant information from a specific knowledge base before generating a response. RAG is how you build AI tools that can answer questions about your specific business — your products, policies, FAQs — rather than just general knowledge.
Sentiment Analysis
An AI technique that identifies and categorises the emotional tone of text — positive, negative, or neutral. Businesses use sentiment analysis to monitor customer reviews, social media mentions, and support tickets to understand how customers feel about their products or services at scale.
Shadow AI
The use of AI tools by employees without the knowledge or approval of their organization's IT or management team. Just as 'shadow IT' described unauthorised software use, shadow AI is a growing concern — staff may be pasting sensitive client data into ChatGPT or using unapproved AI tools, creating data privacy and security risks. Having a clear AI policy in your business is increasingly important.
System Prompt
A set of instructions given to an AI model before a conversation begins, used to define its persona, rules, and behavior. When we build a custom AI chatbot for a business, the system prompt is where we tell it who it is, what it can and can't do, and how it should respond to customers.
Token
The basic unit of text that AI language models process. A token is roughly equivalent to a word or part of a word — 'hamburger' might be split into two tokens, while 'the' is one. AI models have token limits for inputs and outputs, and usage is often billed per token. Understanding tokens helps you use AI tools more efficiently.
Training
The process of teaching an AI model by exposing it to large amounts of data and adjusting its parameters to improve performance. Training a large AI model requires enormous computing power and can take weeks or months. Most businesses use pre-trained models rather than training their own from scratch.
Transformer
The underlying architecture that powers most modern AI language models, including GPT, Claude, and Gemini. Introduced by Google in 2017, the transformer architecture revolutionised AI by enabling models to process and understand context across long sequences of text far more effectively than previous approaches.
Use Case
A specific, practical application of a technology to solve a real problem. In AI, a use case might be 'using AI to automatically respond to common customer enquiries' or 'using AI to summarize meeting notes'. Identifying the right use cases for your business is the first step in any successful AI implementation.
Vector Database
A type of database designed to store and search data in the format that AI models use (vectors/embeddings). Vector databases are a key component of RAG systems — they allow AI tools to quickly find the most relevant information from a large knowledge base when answering a question.
Vibe Coding
A term coined in 2025 describing the practice of building software by describing what you want in plain English to an AI — and letting the AI write the code for you. Instead of learning to code, you 'vibe' with the AI: you describe the idea, review what it builds, give feedback, and iterate. Tools like Cursor, GitHub Copilot, and platforms like the one this website was built on make vibe coding accessible to non-developers. It's one of the most significant shifts in how software gets made.
Voice AI
AI systems that can understand and generate spoken language — enabling voice-based interactions with software. Voice AI powers smart speakers (Alexa, Siri, Google Assistant), AI phone receptionists, and voice-enabled customer service tools. For small businesses, voice AI can handle inbound calls, answer common questions, and route enquiries — 24 hours a day.
Workflow
A sequence of steps or tasks that make up a business process. In the context of AI and automation, a workflow is the series of actions that get triggered and executed automatically — for example, a new lead fills in a form, which triggers an AI to qualify them, send a welcome email, and add them to your CRM.
xAI
The AI company founded by Elon Musk in 2023, responsible for building Grok. xAI's stated mission is to understand the true nature of the universe through AI research. It's one of the newer major players in the AI space and is notable for its integration with the X (formerly Twitter) platform.
Yield (AI Output Quality)
In AI contexts, yield refers to the quality and usefulness of the outputs an AI produces relative to the inputs given. High-yield prompting means getting consistently valuable, accurate, and relevant responses. Improving your yield is largely about better prompts, clearer context, and choosing the right model for the task.
Zero-Shot Learning
The ability of an AI model to perform a task it hasn't been specifically trained on, using only a description or instruction. For example, asking ChatGPT to classify customer feedback into categories it's never seen before. Zero-shot capability is one of the things that makes modern LLMs so versatile for business use.
AI Washing
When a company exaggerates or falsely claims that its product uses AI to appear more innovative or attract investment. Similar to 'greenwashing' in sustainability. As AI becomes a buzzword, AI washing is increasingly common — it's worth asking vendors exactly how AI is used in their product, not just whether it is.
Algorithm
A set of step-by-step instructions a computer follows to complete a task or solve a problem. Think of it like a recipe — the computer follows each step in order to produce a result. Most AI tools are built on complex algorithms that process data and generate outputs.
Artificial Intelligence (AI)
Technology that enables computers to perform tasks that normally require human intelligence — things like understanding language, recognizing images, making decisions, and learning from experience. AI is an umbrella term that covers many different technologies and approaches.
Automation
Using technology to perform tasks with little or no human input. In a business context, automation means setting up systems that handle repetitive work automatically — like sending follow-up emails, updating spreadsheets, or routing customer enquiries — so your team can focus on higher-value work.
Agentic AI
AI that can take actions and make decisions autonomously to complete a goal — not just answer questions, but actually do things like browse the web, send emails, or interact with software on your behalf. Agentic AI is one of the fastest-growing areas of AI development.
AI-Native
A product, business, or workflow that is built from the ground up with AI at its core — rather than having AI bolted on as an afterthought. An AI-native business uses AI as a fundamental part of how it operates, not just as an occasional tool. Increasingly, being AI-native is a competitive advantage.
API (Application Programming Interface)
A way for two software applications to talk to each other. When we integrate an AI tool with your existing software, we're usually using APIs to connect them. You don't need to understand how APIs work — just know they're the plumbing that makes integrations possible.
ChatGPT
The AI assistant built by OpenAI and one of the most widely used AI tools in the world. ChatGPT can write, summarize, answer questions, draft emails, generate ideas, and much more — all through a simple chat interface. It runs on OpenAI's GPT models and is available free or via a paid plan. For most small businesses, it's the best starting point for exploring what AI can do.
Claude
An AI assistant built by Anthropic, widely regarded as one of the best alternatives to ChatGPT. Claude is known for nuanced writing, long-document analysis, and following complex instructions carefully. It's designed with a strong focus on safety and honesty. Available at claude.ai with a free tier and paid plans.
Bot
Short for 'robot' — a software program that performs automated tasks. In business, bots are commonly used for customer service (chatbots), data collection, and repetitive admin tasks. Not all bots use AI, but AI-powered bots are far more capable than simple rule-based ones.
Benchmark
A standardised test used to measure and compare the performance of AI models. When you see claims like 'our AI scores 90% on X benchmark', it means the model was tested against a standard set of problems. Benchmarks help compare different AI tools, though real-world performance can vary.
Black Box
A system where you can see the inputs and outputs but not what happens in between. Most AI models are black boxes — you give them a prompt and get a response, but the internal reasoning is not visible or easily explainable. This is why 'explainable AI' is an active area of research, particularly in regulated industries like finance and healthcare.
Chatbot
A software program designed to simulate conversation with humans, typically via text. Modern AI-powered chatbots (like those built on ChatGPT) can understand natural language, answer complex questions, and handle customer service tasks around the clock — far beyond the simple 'press 1 for billing' bots of the past.
Context Window
The amount of text an AI model can 'see' and consider at one time. Think of it like short-term memory — the larger the context window, the more of a conversation or document the AI can take into account when generating a response. Larger context windows generally produce more coherent, relevant outputs.
Copilot
An AI assistant that works alongside a human to help them do their job better — not replacing them, but augmenting their capabilities. Microsoft's Copilot (built into Office 365) is a well-known example. The term reflects the idea that the human is still in control; the AI is just helping navigate.
Data
Information in a form that computers can process. AI systems learn from data — the more relevant, high-quality data an AI is trained on, the better it tends to perform. In a business context, your data includes things like customer records, transaction history, emails, and documents.
Deepfake
AI-generated video, audio, or images that realistically depict someone saying or doing something they never actually said or did. Deepfakes are created using deep learning techniques and have become increasingly convincing. As a business owner, it's important to be aware of deepfakes — both as a potential security risk (e.g. fake audio of a CEO authorising a transfer) and as a reputational concern.
Deep Learning
A type of machine learning that uses neural networks with many layers to learn from large amounts of data. Deep learning powers many of the most impressive AI capabilities today — including image recognition, speech recognition, and large language models like ChatGPT.
Embedding
A way of representing text, images, or other data as numbers so that an AI can understand relationships between concepts. Embeddings allow AI to understand that 'dog' and 'puppy' are related, or that 'invoice' and 'bill' mean similar things — even without being explicitly told.
Emergent Behavior
Unexpected capabilities that appear in AI models as they grow larger or are trained on more data — abilities that weren't explicitly programmed or anticipated. For example, large language models spontaneously developed the ability to do basic arithmetic and translate languages without being specifically trained to do so. Emergent behavior is one of the reasons AI development can be unpredictable.
Fine-Tuning
The process of taking a pre-trained AI model and training it further on a specific dataset to make it better at a particular task. For example, fine-tuning a general language model on your company's documents and tone of voice so it writes in your style and understands your business context.
Frontier Model
The most advanced AI models available at any given time — the cutting edge of what's technically possible. GPT-4o, Claude 3.5 Sonnet, and Gemini Ultra are examples of frontier models. The term is used in AI policy and safety discussions to describe models powerful enough to potentially pose novel risks alongside their capabilities.
Foundation Model
A large AI model trained on a broad dataset that can be adapted for many different tasks. GPT-4, Claude, and Gemini are all foundation models. They're called 'foundation' models because they serve as the base that other, more specialized AI applications are built on top of.
Gemini
Google's flagship AI assistant and language model, available at gemini.google.com. Gemini integrates natively with Google Workspace — Gmail, Docs, Sheets, Drive — making it a strong choice for businesses already running on Google tools. It competes directly with ChatGPT and Claude and is available with a free tier and paid Google One AI Premium plan.
Grok
An AI assistant built by xAI, Elon Musk's AI company, and integrated into the X (formerly Twitter) platform. Grok is notable for having real-time access to information posted on X and for taking a less filtered approach to responses compared to some competitors. Available to X Premium subscribers and via the Grok standalone app.
Generative AI
AI that can create new content — text, images, audio, video, code — rather than just analysing or classifying existing content. ChatGPT generating a business proposal, DALL-E creating an image from a description, and GitHub Copilot writing code are all examples of generative AI in action.
GPT (Generative Pre-trained Transformer)
The type of AI model that powers ChatGPT, developed by OpenAI. GPT models are trained on vast amounts of text data and can generate human-like text, answer questions, summarize documents, write code, and much more. The 'transformer' part refers to the underlying architecture that makes modern AI so powerful.
Guardrails
Rules, filters, and safety mechanisms built into AI systems to prevent them from producing harmful, offensive, or inappropriate outputs. Guardrails are why AI assistants refuse certain requests. When building custom AI tools for businesses, we also add business-specific guardrails — for example, preventing a customer service bot from making promises the business can't keep.
Hallucination
When an AI confidently produces information that is factually incorrect or completely made up. This is one of the most important limitations of current AI tools to understand. AI doesn't 'know' things the way humans do — it predicts likely text, which can sometimes result in plausible-sounding but wrong answers. Always verify important facts.
Human-in-the-Loop
A system design where humans are involved in reviewing, approving, or correcting AI outputs before they're acted on. For most business applications, keeping a human in the loop is best practice — especially for anything customer-facing, financial, or legally sensitive.
Hyperparameter
Settings that control how an AI model is trained — like how fast it learns, how many training rounds it goes through, or how large its layers are. Unlike regular parameters (which the model learns automatically), hyperparameters are set by the people building the model. Getting them right is part of the craft of AI development.
Inference
The process of using a trained AI model to generate outputs from new inputs. When you type a question into ChatGPT and it responds, that's inference — the model is applying what it learned during training to your specific input. Training happens once; inference happens every time you use the tool.
Integration
Connecting two or more software systems so they can share data and work together. In AI, integration typically means connecting an AI tool to your existing business software — like linking a chatbot to your CRM, or connecting an automation tool to your booking system.
Jailbreak
An attempt to bypass the safety guidelines or restrictions built into an AI model — usually by crafting clever prompts that trick the AI into ignoring its rules. AI companies work continuously to prevent jailbreaks. As a business owner, it's worth knowing this exists so you understand why AI tools sometimes refuse certain requests — those guardrails are there for good reason.
Knowledge Base
A structured collection of information that an AI can draw on to answer questions. When we build a custom AI assistant for your business, we often create a knowledge base containing your FAQs, product details, policies, and procedures — so the AI gives accurate, business-specific answers rather than generic ones.
Large Language Model (LLM)
An AI model trained on enormous amounts of text data that can understand and generate human language. ChatGPT, Claude, and Gemini are all LLMs. They're called 'large' because of the sheer scale of data they're trained on and the number of parameters (internal settings) they contain — often in the billions.
Llama
Meta's family of open-source large language models, released for anyone to download, run, and build on. Unlike ChatGPT or Claude, Llama models can be run locally on your own hardware without sending data to a third-party server — making them popular for privacy-sensitive applications and businesses that want more control over their AI stack.
Machine Learning (ML)
A branch of AI where systems learn from data and improve their performance over time without being explicitly programmed for every scenario. Instead of following rigid rules, machine learning models identify patterns in data and use those patterns to make predictions or decisions.
Multimodal AI
AI that can process and generate multiple types of content — text, images, audio, and video — rather than just one. GPT-4o is multimodal: you can show it a photo and ask a question about it, or have it describe an image. Multimodal AI opens up a much wider range of business applications than text-only models.
Model
In AI, a model is the trained system that takes inputs and produces outputs. When people refer to 'the model', they mean the AI itself — the mathematical structure that has been trained on data. Different models have different strengths, weaknesses, and use cases.
Natural Language Processing (NLP)
The branch of AI focused on enabling computers to understand, interpret, and generate human language. NLP powers chatbots, voice assistants, translation tools, sentiment analysis, and much more. When an AI understands what you're asking in plain English, that's NLP at work.
Neural Network
A type of AI system loosely inspired by the structure of the human brain, made up of layers of interconnected nodes that process information. Neural networks are the foundation of most modern AI — including image recognition, speech recognition, and large language models.
No-Code / Low-Code
Tools and platforms that allow people to build software applications and automations without writing traditional code. Many AI tools are now available in no-code or low-code formats, making them accessible to business owners without technical backgrounds. Zapier and Make are popular examples.
OpenAI
The company behind ChatGPT, GPT-4, and DALL-E. OpenAI is one of the most influential AI research organizations in the world. Founded in 2015, it has become the most recognized name in consumer AI and provides the underlying technology for many third-party AI tools and applications.
Overfitting
When an AI model learns its training data too well — including the noise and quirks — and as a result performs poorly on new, real-world data. Think of it like a student who memorises past exam papers word-for-word but can't answer a question that's phrased slightly differently. Overfitting is a common challenge in building reliable AI systems.
Parameters
The internal settings of an AI model that are adjusted during training to improve performance. A model with more parameters is generally more capable but also more expensive to run. GPT-4 is estimated to have hundreds of billions of parameters — which is part of why it's so capable.
Prompt
The input you give to an AI model — the question, instruction, or context that tells the AI what you want it to do. Writing effective prompts (prompt engineering) is a skill that significantly affects the quality of AI outputs. A well-crafted prompt can be the difference between a mediocre and an excellent result.
Prompt Engineering
The practice of designing and refining prompts to get better outputs from AI models. It involves understanding how AI models interpret instructions and structuring your inputs to guide the model toward the response you want. Good prompt engineering is one of the most practical AI skills a business owner can develop.
Prompt Injection
A type of attack where a malicious user crafts inputs designed to override an AI system's instructions and make it behave in unintended ways. For example, a user might try to trick a customer service chatbot into ignoring its rules by embedding hidden instructions in their message. It's a real security concern for businesses deploying AI-facing tools — and something we account for when building AI systems for clients.
Query
A question or request submitted to an AI system, search engine, or database. In everyday AI use, a query is essentially what you type into ChatGPT or a search bar. In more technical contexts, queries are used to retrieve specific data from databases. The quality of your query directly affects the quality of the answer you get back.
RAG (Retrieval-Augmented Generation)
A technique that combines an AI language model with a search system, allowing the AI to retrieve relevant information from a specific knowledge base before generating a response. RAG is how you build AI tools that can answer questions about your specific business — your products, policies, FAQs — rather than just general knowledge.
Sentiment Analysis
An AI technique that identifies and categorises the emotional tone of text — positive, negative, or neutral. Businesses use sentiment analysis to monitor customer reviews, social media mentions, and support tickets to understand how customers feel about their products or services at scale.
Shadow AI
The use of AI tools by employees without the knowledge or approval of their organization's IT or management team. Just as 'shadow IT' described unauthorised software use, shadow AI is a growing concern — staff may be pasting sensitive client data into ChatGPT or using unapproved AI tools, creating data privacy and security risks. Having a clear AI policy in your business is increasingly important.
System Prompt
A set of instructions given to an AI model before a conversation begins, used to define its persona, rules, and behavior. When we build a custom AI chatbot for a business, the system prompt is where we tell it who it is, what it can and can't do, and how it should respond to customers.
Token
The basic unit of text that AI language models process. A token is roughly equivalent to a word or part of a word — 'hamburger' might be split into two tokens, while 'the' is one. AI models have token limits for inputs and outputs, and usage is often billed per token. Understanding tokens helps you use AI tools more efficiently.
Training
The process of teaching an AI model by exposing it to large amounts of data and adjusting its parameters to improve performance. Training a large AI model requires enormous computing power and can take weeks or months. Most businesses use pre-trained models rather than training their own from scratch.
Transformer
The underlying architecture that powers most modern AI language models, including GPT, Claude, and Gemini. Introduced by Google in 2017, the transformer architecture revolutionised AI by enabling models to process and understand context across long sequences of text far more effectively than previous approaches.
Use Case
A specific, practical application of a technology to solve a real problem. In AI, a use case might be 'using AI to automatically respond to common customer enquiries' or 'using AI to summarize meeting notes'. Identifying the right use cases for your business is the first step in any successful AI implementation.
Vector Database
A type of database designed to store and search data in the format that AI models use (vectors/embeddings). Vector databases are a key component of RAG systems — they allow AI tools to quickly find the most relevant information from a large knowledge base when answering a question.
Vibe Coding
A term coined in 2025 describing the practice of building software by describing what you want in plain English to an AI — and letting the AI write the code for you. Instead of learning to code, you 'vibe' with the AI: you describe the idea, review what it builds, give feedback, and iterate. Tools like Cursor, GitHub Copilot, and platforms like the one this website was built on make vibe coding accessible to non-developers. It's one of the most significant shifts in how software gets made.
Voice AI
AI systems that can understand and generate spoken language — enabling voice-based interactions with software. Voice AI powers smart speakers (Alexa, Siri, Google Assistant), AI phone receptionists, and voice-enabled customer service tools. For small businesses, voice AI can handle inbound calls, answer common questions, and route enquiries — 24 hours a day.
Workflow
A sequence of steps or tasks that make up a business process. In the context of AI and automation, a workflow is the series of actions that get triggered and executed automatically — for example, a new lead fills in a form, which triggers an AI to qualify them, send a welcome email, and add them to your CRM.
xAI
The AI company founded by Elon Musk in 2023, responsible for building Grok. xAI's stated mission is to understand the true nature of the universe through AI research. It's one of the newer major players in the AI space and is notable for its integration with the X (formerly Twitter) platform.
Yield (AI Output Quality)
In AI contexts, yield refers to the quality and usefulness of the outputs an AI produces relative to the inputs given. High-yield prompting means getting consistently valuable, accurate, and relevant responses. Improving your yield is largely about better prompts, clearer context, and choosing the right model for the task.
Zero-Shot Learning
The ability of an AI model to perform a task it hasn't been specifically trained on, using only a description or instruction. For example, asking ChatGPT to classify customer feedback into categories it's never seen before. Zero-shot capability is one of the things that makes modern LLMs so versatile for business use.
AI Washing
When a company exaggerates or falsely claims that its product uses AI to appear more innovative or attract investment. Similar to 'greenwashing' in sustainability. As AI becomes a buzzword, AI washing is increasingly common — it's worth asking vendors exactly how AI is used in their product, not just whether it is.
Algorithm
A set of step-by-step instructions a computer follows to complete a task or solve a problem. Think of it like a recipe — the computer follows each step in order to produce a result. Most AI tools are built on complex algorithms that process data and generate outputs.
Artificial Intelligence (AI)
Technology that enables computers to perform tasks that normally require human intelligence — things like understanding language, recognizing images, making decisions, and learning from experience. AI is an umbrella term that covers many different technologies and approaches.
Automation
Using technology to perform tasks with little or no human input. In a business context, automation means setting up systems that handle repetitive work automatically — like sending follow-up emails, updating spreadsheets, or routing customer enquiries — so your team can focus on higher-value work.
Agentic AI
AI that can take actions and make decisions autonomously to complete a goal — not just answer questions, but actually do things like browse the web, send emails, or interact with software on your behalf. Agentic AI is one of the fastest-growing areas of AI development.
AI-Native
A product, business, or workflow that is built from the ground up with AI at its core — rather than having AI bolted on as an afterthought. An AI-native business uses AI as a fundamental part of how it operates, not just as an occasional tool. Increasingly, being AI-native is a competitive advantage.
API (Application Programming Interface)
A way for two software applications to talk to each other. When we integrate an AI tool with your existing software, we're usually using APIs to connect them. You don't need to understand how APIs work — just know they're the plumbing that makes integrations possible.
ChatGPT
The AI assistant built by OpenAI and one of the most widely used AI tools in the world. ChatGPT can write, summarize, answer questions, draft emails, generate ideas, and much more — all through a simple chat interface. It runs on OpenAI's GPT models and is available free or via a paid plan. For most small businesses, it's the best starting point for exploring what AI can do.
Claude
An AI assistant built by Anthropic, widely regarded as one of the best alternatives to ChatGPT. Claude is known for nuanced writing, long-document analysis, and following complex instructions carefully. It's designed with a strong focus on safety and honesty. Available at claude.ai with a free tier and paid plans.
Bot
Short for 'robot' — a software program that performs automated tasks. In business, bots are commonly used for customer service (chatbots), data collection, and repetitive admin tasks. Not all bots use AI, but AI-powered bots are far more capable than simple rule-based ones.
Benchmark
A standardised test used to measure and compare the performance of AI models. When you see claims like 'our AI scores 90% on X benchmark', it means the model was tested against a standard set of problems. Benchmarks help compare different AI tools, though real-world performance can vary.
Black Box
A system where you can see the inputs and outputs but not what happens in between. Most AI models are black boxes — you give them a prompt and get a response, but the internal reasoning is not visible or easily explainable. This is why 'explainable AI' is an active area of research, particularly in regulated industries like finance and healthcare.
Chatbot
A software program designed to simulate conversation with humans, typically via text. Modern AI-powered chatbots (like those built on ChatGPT) can understand natural language, answer complex questions, and handle customer service tasks around the clock — far beyond the simple 'press 1 for billing' bots of the past.
Context Window
The amount of text an AI model can 'see' and consider at one time. Think of it like short-term memory — the larger the context window, the more of a conversation or document the AI can take into account when generating a response. Larger context windows generally produce more coherent, relevant outputs.
Copilot
An AI assistant that works alongside a human to help them do their job better — not replacing them, but augmenting their capabilities. Microsoft's Copilot (built into Office 365) is a well-known example. The term reflects the idea that the human is still in control; the AI is just helping navigate.
Data
Information in a form that computers can process. AI systems learn from data — the more relevant, high-quality data an AI is trained on, the better it tends to perform. In a business context, your data includes things like customer records, transaction history, emails, and documents.
Deepfake
AI-generated video, audio, or images that realistically depict someone saying or doing something they never actually said or did. Deepfakes are created using deep learning techniques and have become increasingly convincing. As a business owner, it's important to be aware of deepfakes — both as a potential security risk (e.g. fake audio of a CEO authorising a transfer) and as a reputational concern.
Deep Learning
A type of machine learning that uses neural networks with many layers to learn from large amounts of data. Deep learning powers many of the most impressive AI capabilities today — including image recognition, speech recognition, and large language models like ChatGPT.
Embedding
A way of representing text, images, or other data as numbers so that an AI can understand relationships between concepts. Embeddings allow AI to understand that 'dog' and 'puppy' are related, or that 'invoice' and 'bill' mean similar things — even without being explicitly told.
Emergent Behavior
Unexpected capabilities that appear in AI models as they grow larger or are trained on more data — abilities that weren't explicitly programmed or anticipated. For example, large language models spontaneously developed the ability to do basic arithmetic and translate languages without being specifically trained to do so. Emergent behavior is one of the reasons AI development can be unpredictable.
Fine-Tuning
The process of taking a pre-trained AI model and training it further on a specific dataset to make it better at a particular task. For example, fine-tuning a general language model on your company's documents and tone of voice so it writes in your style and understands your business context.
Frontier Model
The most advanced AI models available at any given time — the cutting edge of what's technically possible. GPT-4o, Claude 3.5 Sonnet, and Gemini Ultra are examples of frontier models. The term is used in AI policy and safety discussions to describe models powerful enough to potentially pose novel risks alongside their capabilities.
Foundation Model
A large AI model trained on a broad dataset that can be adapted for many different tasks. GPT-4, Claude, and Gemini are all foundation models. They're called 'foundation' models because they serve as the base that other, more specialized AI applications are built on top of.
Gemini
Google's flagship AI assistant and language model, available at gemini.google.com. Gemini integrates natively with Google Workspace — Gmail, Docs, Sheets, Drive — making it a strong choice for businesses already running on Google tools. It competes directly with ChatGPT and Claude and is available with a free tier and paid Google One AI Premium plan.
Grok
An AI assistant built by xAI, Elon Musk's AI company, and integrated into the X (formerly Twitter) platform. Grok is notable for having real-time access to information posted on X and for taking a less filtered approach to responses compared to some competitors. Available to X Premium subscribers and via the Grok standalone app.
Generative AI
AI that can create new content — text, images, audio, video, code — rather than just analysing or classifying existing content. ChatGPT generating a business proposal, DALL-E creating an image from a description, and GitHub Copilot writing code are all examples of generative AI in action.
GPT (Generative Pre-trained Transformer)
The type of AI model that powers ChatGPT, developed by OpenAI. GPT models are trained on vast amounts of text data and can generate human-like text, answer questions, summarize documents, write code, and much more. The 'transformer' part refers to the underlying architecture that makes modern AI so powerful.
Guardrails
Rules, filters, and safety mechanisms built into AI systems to prevent them from producing harmful, offensive, or inappropriate outputs. Guardrails are why AI assistants refuse certain requests. When building custom AI tools for businesses, we also add business-specific guardrails — for example, preventing a customer service bot from making promises the business can't keep.
Hallucination
When an AI confidently produces information that is factually incorrect or completely made up. This is one of the most important limitations of current AI tools to understand. AI doesn't 'know' things the way humans do — it predicts likely text, which can sometimes result in plausible-sounding but wrong answers. Always verify important facts.
Human-in-the-Loop
A system design where humans are involved in reviewing, approving, or correcting AI outputs before they're acted on. For most business applications, keeping a human in the loop is best practice — especially for anything customer-facing, financial, or legally sensitive.
Hyperparameter
Settings that control how an AI model is trained — like how fast it learns, how many training rounds it goes through, or how large its layers are. Unlike regular parameters (which the model learns automatically), hyperparameters are set by the people building the model. Getting them right is part of the craft of AI development.
Inference
The process of using a trained AI model to generate outputs from new inputs. When you type a question into ChatGPT and it responds, that's inference — the model is applying what it learned during training to your specific input. Training happens once; inference happens every time you use the tool.
Integration
Connecting two or more software systems so they can share data and work together. In AI, integration typically means connecting an AI tool to your existing business software — like linking a chatbot to your CRM, or connecting an automation tool to your booking system.
Jailbreak
An attempt to bypass the safety guidelines or restrictions built into an AI model — usually by crafting clever prompts that trick the AI into ignoring its rules. AI companies work continuously to prevent jailbreaks. As a business owner, it's worth knowing this exists so you understand why AI tools sometimes refuse certain requests — those guardrails are there for good reason.
Knowledge Base
A structured collection of information that an AI can draw on to answer questions. When we build a custom AI assistant for your business, we often create a knowledge base containing your FAQs, product details, policies, and procedures — so the AI gives accurate, business-specific answers rather than generic ones.
Large Language Model (LLM)
An AI model trained on enormous amounts of text data that can understand and generate human language. ChatGPT, Claude, and Gemini are all LLMs. They're called 'large' because of the sheer scale of data they're trained on and the number of parameters (internal settings) they contain — often in the billions.
Llama
Meta's family of open-source large language models, released for anyone to download, run, and build on. Unlike ChatGPT or Claude, Llama models can be run locally on your own hardware without sending data to a third-party server — making them popular for privacy-sensitive applications and businesses that want more control over their AI stack.
Machine Learning (ML)
A branch of AI where systems learn from data and improve their performance over time without being explicitly programmed for every scenario. Instead of following rigid rules, machine learning models identify patterns in data and use those patterns to make predictions or decisions.
Multimodal AI
AI that can process and generate multiple types of content — text, images, audio, and video — rather than just one. GPT-4o is multimodal: you can show it a photo and ask a question about it, or have it describe an image. Multimodal AI opens up a much wider range of business applications than text-only models.
Model
In AI, a model is the trained system that takes inputs and produces outputs. When people refer to 'the model', they mean the AI itself — the mathematical structure that has been trained on data. Different models have different strengths, weaknesses, and use cases.
Natural Language Processing (NLP)
The branch of AI focused on enabling computers to understand, interpret, and generate human language. NLP powers chatbots, voice assistants, translation tools, sentiment analysis, and much more. When an AI understands what you're asking in plain English, that's NLP at work.
Neural Network
A type of AI system loosely inspired by the structure of the human brain, made up of layers of interconnected nodes that process information. Neural networks are the foundation of most modern AI — including image recognition, speech recognition, and large language models.
No-Code / Low-Code
Tools and platforms that allow people to build software applications and automations without writing traditional code. Many AI tools are now available in no-code or low-code formats, making them accessible to business owners without technical backgrounds. Zapier and Make are popular examples.
OpenAI
The company behind ChatGPT, GPT-4, and DALL-E. OpenAI is one of the most influential AI research organizations in the world. Founded in 2015, it has become the most recognized name in consumer AI and provides the underlying technology for many third-party AI tools and applications.
Overfitting
When an AI model learns its training data too well — including the noise and quirks — and as a result performs poorly on new, real-world data. Think of it like a student who memorises past exam papers word-for-word but can't answer a question that's phrased slightly differently. Overfitting is a common challenge in building reliable AI systems.
Parameters
The internal settings of an AI model that are adjusted during training to improve performance. A model with more parameters is generally more capable but also more expensive to run. GPT-4 is estimated to have hundreds of billions of parameters — which is part of why it's so capable.
Prompt
The input you give to an AI model — the question, instruction, or context that tells the AI what you want it to do. Writing effective prompts (prompt engineering) is a skill that significantly affects the quality of AI outputs. A well-crafted prompt can be the difference between a mediocre and an excellent result.
Prompt Engineering
The practice of designing and refining prompts to get better outputs from AI models. It involves understanding how AI models interpret instructions and structuring your inputs to guide the model toward the response you want. Good prompt engineering is one of the most practical AI skills a business owner can develop.
Prompt Injection
A type of attack where a malicious user crafts inputs designed to override an AI system's instructions and make it behave in unintended ways. For example, a user might try to trick a customer service chatbot into ignoring its rules by embedding hidden instructions in their message. It's a real security concern for businesses deploying AI-facing tools — and something we account for when building AI systems for clients.
Query
A question or request submitted to an AI system, search engine, or database. In everyday AI use, a query is essentially what you type into ChatGPT or a search bar. In more technical contexts, queries are used to retrieve specific data from databases. The quality of your query directly affects the quality of the answer you get back.
RAG (Retrieval-Augmented Generation)
A technique that combines an AI language model with a search system, allowing the AI to retrieve relevant information from a specific knowledge base before generating a response. RAG is how you build AI tools that can answer questions about your specific business — your products, policies, FAQs — rather than just general knowledge.
Sentiment Analysis
An AI technique that identifies and categorises the emotional tone of text — positive, negative, or neutral. Businesses use sentiment analysis to monitor customer reviews, social media mentions, and support tickets to understand how customers feel about their products or services at scale.
Shadow AI
The use of AI tools by employees without the knowledge or approval of their organization's IT or management team. Just as 'shadow IT' described unauthorised software use, shadow AI is a growing concern — staff may be pasting sensitive client data into ChatGPT or using unapproved AI tools, creating data privacy and security risks. Having a clear AI policy in your business is increasingly important.
System Prompt
A set of instructions given to an AI model before a conversation begins, used to define its persona, rules, and behavior. When we build a custom AI chatbot for a business, the system prompt is where we tell it who it is, what it can and can't do, and how it should respond to customers.
Token
The basic unit of text that AI language models process. A token is roughly equivalent to a word or part of a word — 'hamburger' might be split into two tokens, while 'the' is one. AI models have token limits for inputs and outputs, and usage is often billed per token. Understanding tokens helps you use AI tools more efficiently.
Training
The process of teaching an AI model by exposing it to large amounts of data and adjusting its parameters to improve performance. Training a large AI model requires enormous computing power and can take weeks or months. Most businesses use pre-trained models rather than training their own from scratch.
Transformer
The underlying architecture that powers most modern AI language models, including GPT, Claude, and Gemini. Introduced by Google in 2017, the transformer architecture revolutionised AI by enabling models to process and understand context across long sequences of text far more effectively than previous approaches.
Use Case
A specific, practical application of a technology to solve a real problem. In AI, a use case might be 'using AI to automatically respond to common customer enquiries' or 'using AI to summarize meeting notes'. Identifying the right use cases for your business is the first step in any successful AI implementation.
Vector Database
A type of database designed to store and search data in the format that AI models use (vectors/embeddings). Vector databases are a key component of RAG systems — they allow AI tools to quickly find the most relevant information from a large knowledge base when answering a question.
Vibe Coding
A term coined in 2025 describing the practice of building software by describing what you want in plain English to an AI — and letting the AI write the code for you. Instead of learning to code, you 'vibe' with the AI: you describe the idea, review what it builds, give feedback, and iterate. Tools like Cursor, GitHub Copilot, and platforms like the one this website was built on make vibe coding accessible to non-developers. It's one of the most significant shifts in how software gets made.
Voice AI
AI systems that can understand and generate spoken language — enabling voice-based interactions with software. Voice AI powers smart speakers (Alexa, Siri, Google Assistant), AI phone receptionists, and voice-enabled customer service tools. For small businesses, voice AI can handle inbound calls, answer common questions, and route enquiries — 24 hours a day.
Workflow
A sequence of steps or tasks that make up a business process. In the context of AI and automation, a workflow is the series of actions that get triggered and executed automatically — for example, a new lead fills in a form, which triggers an AI to qualify them, send a welcome email, and add them to your CRM.
xAI
The AI company founded by Elon Musk in 2023, responsible for building Grok. xAI's stated mission is to understand the true nature of the universe through AI research. It's one of the newer major players in the AI space and is notable for its integration with the X (formerly Twitter) platform.
Yield (AI Output Quality)
In AI contexts, yield refers to the quality and usefulness of the outputs an AI produces relative to the inputs given. High-yield prompting means getting consistently valuable, accurate, and relevant responses. Improving your yield is largely about better prompts, clearer context, and choosing the right model for the task.
Zero-Shot Learning
The ability of an AI model to perform a task it hasn't been specifically trained on, using only a description or instruction. For example, asking ChatGPT to classify customer feedback into categories it's never seen before. Zero-shot capability is one of the things that makes modern LLMs so versatile for business use.
AI Washing
When a company exaggerates or falsely claims that its product uses AI to appear more innovative or attract investment. Similar to 'greenwashing' in sustainability. As AI becomes a buzzword, AI washing is increasingly common — it's worth asking vendors exactly how AI is used in their product, not just whether it is.
Algorithm
A set of step-by-step instructions a computer follows to complete a task or solve a problem. Think of it like a recipe — the computer follows each step in order to produce a result. Most AI tools are built on complex algorithms that process data and generate outputs.
Artificial Intelligence (AI)
Technology that enables computers to perform tasks that normally require human intelligence — things like understanding language, recognizing images, making decisions, and learning from experience. AI is an umbrella term that covers many different technologies and approaches.
Automation
Using technology to perform tasks with little or no human input. In a business context, automation means setting up systems that handle repetitive work automatically — like sending follow-up emails, updating spreadsheets, or routing customer enquiries — so your team can focus on higher-value work.
Agentic AI
AI that can take actions and make decisions autonomously to complete a goal — not just answer questions, but actually do things like browse the web, send emails, or interact with software on your behalf. Agentic AI is one of the fastest-growing areas of AI development.
AI-Native
A product, business, or workflow that is built from the ground up with AI at its core — rather than having AI bolted on as an afterthought. An AI-native business uses AI as a fundamental part of how it operates, not just as an occasional tool. Increasingly, being AI-native is a competitive advantage.
API (Application Programming Interface)
A way for two software applications to talk to each other. When we integrate an AI tool with your existing software, we're usually using APIs to connect them. You don't need to understand how APIs work — just know they're the plumbing that makes integrations possible.
ChatGPT
The AI assistant built by OpenAI and one of the most widely used AI tools in the world. ChatGPT can write, summarize, answer questions, draft emails, generate ideas, and much more — all through a simple chat interface. It runs on OpenAI's GPT models and is available free or via a paid plan. For most small businesses, it's the best starting point for exploring what AI can do.
Claude
An AI assistant built by Anthropic, widely regarded as one of the best alternatives to ChatGPT. Claude is known for nuanced writing, long-document analysis, and following complex instructions carefully. It's designed with a strong focus on safety and honesty. Available at claude.ai with a free tier and paid plans.
Bot
Short for 'robot' — a software program that performs automated tasks. In business, bots are commonly used for customer service (chatbots), data collection, and repetitive admin tasks. Not all bots use AI, but AI-powered bots are far more capable than simple rule-based ones.
Benchmark
A standardised test used to measure and compare the performance of AI models. When you see claims like 'our AI scores 90% on X benchmark', it means the model was tested against a standard set of problems. Benchmarks help compare different AI tools, though real-world performance can vary.
Black Box
A system where you can see the inputs and outputs but not what happens in between. Most AI models are black boxes — you give them a prompt and get a response, but the internal reasoning is not visible or easily explainable. This is why 'explainable AI' is an active area of research, particularly in regulated industries like finance and healthcare.
Chatbot
A software program designed to simulate conversation with humans, typically via text. Modern AI-powered chatbots (like those built on ChatGPT) can understand natural language, answer complex questions, and handle customer service tasks around the clock — far beyond the simple 'press 1 for billing' bots of the past.
Context Window
The amount of text an AI model can 'see' and consider at one time. Think of it like short-term memory — the larger the context window, the more of a conversation or document the AI can take into account when generating a response. Larger context windows generally produce more coherent, relevant outputs.
Copilot
An AI assistant that works alongside a human to help them do their job better — not replacing them, but augmenting their capabilities. Microsoft's Copilot (built into Office 365) is a well-known example. The term reflects the idea that the human is still in control; the AI is just helping navigate.
Data
Information in a form that computers can process. AI systems learn from data — the more relevant, high-quality data an AI is trained on, the better it tends to perform. In a business context, your data includes things like customer records, transaction history, emails, and documents.
Deepfake
AI-generated video, audio, or images that realistically depict someone saying or doing something they never actually said or did. Deepfakes are created using deep learning techniques and have become increasingly convincing. As a business owner, it's important to be aware of deepfakes — both as a potential security risk (e.g. fake audio of a CEO authorising a transfer) and as a reputational concern.
Deep Learning
A type of machine learning that uses neural networks with many layers to learn from large amounts of data. Deep learning powers many of the most impressive AI capabilities today — including image recognition, speech recognition, and large language models like ChatGPT.
Embedding
A way of representing text, images, or other data as numbers so that an AI can understand relationships between concepts. Embeddings allow AI to understand that 'dog' and 'puppy' are related, or that 'invoice' and 'bill' mean similar things — even without being explicitly told.
Emergent Behavior
Unexpected capabilities that appear in AI models as they grow larger or are trained on more data — abilities that weren't explicitly programmed or anticipated. For example, large language models spontaneously developed the ability to do basic arithmetic and translate languages without being specifically trained to do so. Emergent behavior is one of the reasons AI development can be unpredictable.
Fine-Tuning
The process of taking a pre-trained AI model and training it further on a specific dataset to make it better at a particular task. For example, fine-tuning a general language model on your company's documents and tone of voice so it writes in your style and understands your business context.
Frontier Model
The most advanced AI models available at any given time — the cutting edge of what's technically possible. GPT-4o, Claude 3.5 Sonnet, and Gemini Ultra are examples of frontier models. The term is used in AI policy and safety discussions to describe models powerful enough to potentially pose novel risks alongside their capabilities.
Foundation Model
A large AI model trained on a broad dataset that can be adapted for many different tasks. GPT-4, Claude, and Gemini are all foundation models. They're called 'foundation' models because they serve as the base that other, more specialized AI applications are built on top of.
Gemini
Google's flagship AI assistant and language model, available at gemini.google.com. Gemini integrates natively with Google Workspace — Gmail, Docs, Sheets, Drive — making it a strong choice for businesses already running on Google tools. It competes directly with ChatGPT and Claude and is available with a free tier and paid Google One AI Premium plan.
Grok
An AI assistant built by xAI, Elon Musk's AI company, and integrated into the X (formerly Twitter) platform. Grok is notable for having real-time access to information posted on X and for taking a less filtered approach to responses compared to some competitors. Available to X Premium subscribers and via the Grok standalone app.
Generative AI
AI that can create new content — text, images, audio, video, code — rather than just analysing or classifying existing content. ChatGPT generating a business proposal, DALL-E creating an image from a description, and GitHub Copilot writing code are all examples of generative AI in action.
GPT (Generative Pre-trained Transformer)
The type of AI model that powers ChatGPT, developed by OpenAI. GPT models are trained on vast amounts of text data and can generate human-like text, answer questions, summarize documents, write code, and much more. The 'transformer' part refers to the underlying architecture that makes modern AI so powerful.
Guardrails
Rules, filters, and safety mechanisms built into AI systems to prevent them from producing harmful, offensive, or inappropriate outputs. Guardrails are why AI assistants refuse certain requests. When building custom AI tools for businesses, we also add business-specific guardrails — for example, preventing a customer service bot from making promises the business can't keep.
Hallucination
When an AI confidently produces information that is factually incorrect or completely made up. This is one of the most important limitations of current AI tools to understand. AI doesn't 'know' things the way humans do — it predicts likely text, which can sometimes result in plausible-sounding but wrong answers. Always verify important facts.
Human-in-the-Loop
A system design where humans are involved in reviewing, approving, or correcting AI outputs before they're acted on. For most business applications, keeping a human in the loop is best practice — especially for anything customer-facing, financial, or legally sensitive.
Hyperparameter
Settings that control how an AI model is trained — like how fast it learns, how many training rounds it goes through, or how large its layers are. Unlike regular parameters (which the model learns automatically), hyperparameters are set by the people building the model. Getting them right is part of the craft of AI development.
Inference
The process of using a trained AI model to generate outputs from new inputs. When you type a question into ChatGPT and it responds, that's inference — the model is applying what it learned during training to your specific input. Training happens once; inference happens every time you use the tool.
Integration
Connecting two or more software systems so they can share data and work together. In AI, integration typically means connecting an AI tool to your existing business software — like linking a chatbot to your CRM, or connecting an automation tool to your booking system.
Jailbreak
An attempt to bypass the safety guidelines or restrictions built into an AI model — usually by crafting clever prompts that trick the AI into ignoring its rules. AI companies work continuously to prevent jailbreaks. As a business owner, it's worth knowing this exists so you understand why AI tools sometimes refuse certain requests — those guardrails are there for good reason.
Knowledge Base
A structured collection of information that an AI can draw on to answer questions. When we build a custom AI assistant for your business, we often create a knowledge base containing your FAQs, product details, policies, and procedures — so the AI gives accurate, business-specific answers rather than generic ones.
Large Language Model (LLM)
An AI model trained on enormous amounts of text data that can understand and generate human language. ChatGPT, Claude, and Gemini are all LLMs. They're called 'large' because of the sheer scale of data they're trained on and the number of parameters (internal settings) they contain — often in the billions.
Llama
Meta's family of open-source large language models, released for anyone to download, run, and build on. Unlike ChatGPT or Claude, Llama models can be run locally on your own hardware without sending data to a third-party server — making them popular for privacy-sensitive applications and businesses that want more control over their AI stack.
Machine Learning (ML)
A branch of AI where systems learn from data and improve their performance over time without being explicitly programmed for every scenario. Instead of following rigid rules, machine learning models identify patterns in data and use those patterns to make predictions or decisions.
Multimodal AI
AI that can process and generate multiple types of content — text, images, audio, and video — rather than just one. GPT-4o is multimodal: you can show it a photo and ask a question about it, or have it describe an image. Multimodal AI opens up a much wider range of business applications than text-only models.
Model
In AI, a model is the trained system that takes inputs and produces outputs. When people refer to 'the model', they mean the AI itself — the mathematical structure that has been trained on data. Different models have different strengths, weaknesses, and use cases.
Natural Language Processing (NLP)
The branch of AI focused on enabling computers to understand, interpret, and generate human language. NLP powers chatbots, voice assistants, translation tools, sentiment analysis, and much more. When an AI understands what you're asking in plain English, that's NLP at work.
Neural Network
A type of AI system loosely inspired by the structure of the human brain, made up of layers of interconnected nodes that process information. Neural networks are the foundation of most modern AI — including image recognition, speech recognition, and large language models.
No-Code / Low-Code
Tools and platforms that allow people to build software applications and automations without writing traditional code. Many AI tools are now available in no-code or low-code formats, making them accessible to business owners without technical backgrounds. Zapier and Make are popular examples.
OpenAI
The company behind ChatGPT, GPT-4, and DALL-E. OpenAI is one of the most influential AI research organizations in the world. Founded in 2015, it has become the most recognized name in consumer AI and provides the underlying technology for many third-party AI tools and applications.
Overfitting
When an AI model learns its training data too well — including the noise and quirks — and as a result performs poorly on new, real-world data. Think of it like a student who memorises past exam papers word-for-word but can't answer a question that's phrased slightly differently. Overfitting is a common challenge in building reliable AI systems.
Parameters
The internal settings of an AI model that are adjusted during training to improve performance. A model with more parameters is generally more capable but also more expensive to run. GPT-4 is estimated to have hundreds of billions of parameters — which is part of why it's so capable.
Prompt
The input you give to an AI model — the question, instruction, or context that tells the AI what you want it to do. Writing effective prompts (prompt engineering) is a skill that significantly affects the quality of AI outputs. A well-crafted prompt can be the difference between a mediocre and an excellent result.
Prompt Engineering
The practice of designing and refining prompts to get better outputs from AI models. It involves understanding how AI models interpret instructions and structuring your inputs to guide the model toward the response you want. Good prompt engineering is one of the most practical AI skills a business owner can develop.
Prompt Injection
A type of attack where a malicious user crafts inputs designed to override an AI system's instructions and make it behave in unintended ways. For example, a user might try to trick a customer service chatbot into ignoring its rules by embedding hidden instructions in their message. It's a real security concern for businesses deploying AI-facing tools — and something we account for when building AI systems for clients.
Query
A question or request submitted to an AI system, search engine, or database. In everyday AI use, a query is essentially what you type into ChatGPT or a search bar. In more technical contexts, queries are used to retrieve specific data from databases. The quality of your query directly affects the quality of the answer you get back.
RAG (Retrieval-Augmented Generation)
A technique that combines an AI language model with a search system, allowing the AI to retrieve relevant information from a specific knowledge base before generating a response. RAG is how you build AI tools that can answer questions about your specific business — your products, policies, FAQs — rather than just general knowledge.
Sentiment Analysis
An AI technique that identifies and categorises the emotional tone of text — positive, negative, or neutral. Businesses use sentiment analysis to monitor customer reviews, social media mentions, and support tickets to understand how customers feel about their products or services at scale.
Shadow AI
The use of AI tools by employees without the knowledge or approval of their organization's IT or management team. Just as 'shadow IT' described unauthorised software use, shadow AI is a growing concern — staff may be pasting sensitive client data into ChatGPT or using unapproved AI tools, creating data privacy and security risks. Having a clear AI policy in your business is increasingly important.
System Prompt
A set of instructions given to an AI model before a conversation begins, used to define its persona, rules, and behavior. When we build a custom AI chatbot for a business, the system prompt is where we tell it who it is, what it can and can't do, and how it should respond to customers.
Token
The basic unit of text that AI language models process. A token is roughly equivalent to a word or part of a word — 'hamburger' might be split into two tokens, while 'the' is one. AI models have token limits for inputs and outputs, and usage is often billed per token. Understanding tokens helps you use AI tools more efficiently.
Training
The process of teaching an AI model by exposing it to large amounts of data and adjusting its parameters to improve performance. Training a large AI model requires enormous computing power and can take weeks or months. Most businesses use pre-trained models rather than training their own from scratch.
Transformer
The underlying architecture that powers most modern AI language models, including GPT, Claude, and Gemini. Introduced by Google in 2017, the transformer architecture revolutionised AI by enabling models to process and understand context across long sequences of text far more effectively than previous approaches.
Use Case
A specific, practical application of a technology to solve a real problem. In AI, a use case might be 'using AI to automatically respond to common customer enquiries' or 'using AI to summarize meeting notes'. Identifying the right use cases for your business is the first step in any successful AI implementation.
Vector Database
A type of database designed to store and search data in the format that AI models use (vectors/embeddings). Vector databases are a key component of RAG systems — they allow AI tools to quickly find the most relevant information from a large knowledge base when answering a question.
Vibe Coding
A term coined in 2025 describing the practice of building software by describing what you want in plain English to an AI — and letting the AI write the code for you. Instead of learning to code, you 'vibe' with the AI: you describe the idea, review what it builds, give feedback, and iterate. Tools like Cursor, GitHub Copilot, and platforms like the one this website was built on make vibe coding accessible to non-developers. It's one of the most significant shifts in how software gets made.
Voice AI
AI systems that can understand and generate spoken language — enabling voice-based interactions with software. Voice AI powers smart speakers (Alexa, Siri, Google Assistant), AI phone receptionists, and voice-enabled customer service tools. For small businesses, voice AI can handle inbound calls, answer common questions, and route enquiries — 24 hours a day.
Workflow
A sequence of steps or tasks that make up a business process. In the context of AI and automation, a workflow is the series of actions that get triggered and executed automatically — for example, a new lead fills in a form, which triggers an AI to qualify them, send a welcome email, and add them to your CRM.
xAI
The AI company founded by Elon Musk in 2023, responsible for building Grok. xAI's stated mission is to understand the true nature of the universe through AI research. It's one of the newer major players in the AI space and is notable for its integration with the X (formerly Twitter) platform.
Yield (AI Output Quality)
In AI contexts, yield refers to the quality and usefulness of the outputs an AI produces relative to the inputs given. High-yield prompting means getting consistently valuable, accurate, and relevant responses. Improving your yield is largely about better prompts, clearer context, and choosing the right model for the task.
Zero-Shot Learning
The ability of an AI model to perform a task it hasn't been specifically trained on, using only a description or instruction. For example, asking ChatGPT to classify customer feedback into categories it's never seen before. Zero-shot capability is one of the things that makes modern LLMs so versatile for business use.
AI Washing
When a company exaggerates or falsely claims that its product uses AI to appear more innovative or attract investment. Similar to 'greenwashing' in sustainability. As AI becomes a buzzword, AI washing is increasingly common — it's worth asking vendors exactly how AI is used in their product, not just whether it is.
Algorithm
A set of step-by-step instructions a computer follows to complete a task or solve a problem. Think of it like a recipe — the computer follows each step in order to produce a result. Most AI tools are built on complex algorithms that process data and generate outputs.
Artificial Intelligence (AI)
Technology that enables computers to perform tasks that normally require human intelligence — things like understanding language, recognizing images, making decisions, and learning from experience. AI is an umbrella term that covers many different technologies and approaches.
Automation
Using technology to perform tasks with little or no human input. In a business context, automation means setting up systems that handle repetitive work automatically — like sending follow-up emails, updating spreadsheets, or routing customer enquiries — so your team can focus on higher-value work.
Agentic AI
AI that can take actions and make decisions autonomously to complete a goal — not just answer questions, but actually do things like browse the web, send emails, or interact with software on your behalf. Agentic AI is one of the fastest-growing areas of AI development.
AI-Native
A product, business, or workflow that is built from the ground up with AI at its core — rather than having AI bolted on as an afterthought. An AI-native business uses AI as a fundamental part of how it operates, not just as an occasional tool. Increasingly, being AI-native is a competitive advantage.
API (Application Programming Interface)
A way for two software applications to talk to each other. When we integrate an AI tool with your existing software, we're usually using APIs to connect them. You don't need to understand how APIs work — just know they're the plumbing that makes integrations possible.
ChatGPT
The AI assistant built by OpenAI and one of the most widely used AI tools in the world. ChatGPT can write, summarize, answer questions, draft emails, generate ideas, and much more — all through a simple chat interface. It runs on OpenAI's GPT models and is available free or via a paid plan. For most small businesses, it's the best starting point for exploring what AI can do.
Claude
An AI assistant built by Anthropic, widely regarded as one of the best alternatives to ChatGPT. Claude is known for nuanced writing, long-document analysis, and following complex instructions carefully. It's designed with a strong focus on safety and honesty. Available at claude.ai with a free tier and paid plans.
Bot
Short for 'robot' — a software program that performs automated tasks. In business, bots are commonly used for customer service (chatbots), data collection, and repetitive admin tasks. Not all bots use AI, but AI-powered bots are far more capable than simple rule-based ones.
Benchmark
A standardised test used to measure and compare the performance of AI models. When you see claims like 'our AI scores 90% on X benchmark', it means the model was tested against a standard set of problems. Benchmarks help compare different AI tools, though real-world performance can vary.
Black Box
A system where you can see the inputs and outputs but not what happens in between. Most AI models are black boxes — you give them a prompt and get a response, but the internal reasoning is not visible or easily explainable. This is why 'explainable AI' is an active area of research, particularly in regulated industries like finance and healthcare.
Chatbot
A software program designed to simulate conversation with humans, typically via text. Modern AI-powered chatbots (like those built on ChatGPT) can understand natural language, answer complex questions, and handle customer service tasks around the clock — far beyond the simple 'press 1 for billing' bots of the past.
Context Window
The amount of text an AI model can 'see' and consider at one time. Think of it like short-term memory — the larger the context window, the more of a conversation or document the AI can take into account when generating a response. Larger context windows generally produce more coherent, relevant outputs.
Copilot
An AI assistant that works alongside a human to help them do their job better — not replacing them, but augmenting their capabilities. Microsoft's Copilot (built into Office 365) is a well-known example. The term reflects the idea that the human is still in control; the AI is just helping navigate.
Data
Information in a form that computers can process. AI systems learn from data — the more relevant, high-quality data an AI is trained on, the better it tends to perform. In a business context, your data includes things like customer records, transaction history, emails, and documents.
Deepfake
AI-generated video, audio, or images that realistically depict someone saying or doing something they never actually said or did. Deepfakes are created using deep learning techniques and have become increasingly convincing. As a business owner, it's important to be aware of deepfakes — both as a potential security risk (e.g. fake audio of a CEO authorising a transfer) and as a reputational concern.
Deep Learning
A type of machine learning that uses neural networks with many layers to learn from large amounts of data. Deep learning powers many of the most impressive AI capabilities today — including image recognition, speech recognition, and large language models like ChatGPT.
Embedding
A way of representing text, images, or other data as numbers so that an AI can understand relationships between concepts. Embeddings allow AI to understand that 'dog' and 'puppy' are related, or that 'invoice' and 'bill' mean similar things — even without being explicitly told.
Emergent Behavior
Unexpected capabilities that appear in AI models as they grow larger or are trained on more data — abilities that weren't explicitly programmed or anticipated. For example, large language models spontaneously developed the ability to do basic arithmetic and translate languages without being specifically trained to do so. Emergent behavior is one of the reasons AI development can be unpredictable.
Fine-Tuning
The process of taking a pre-trained AI model and training it further on a specific dataset to make it better at a particular task. For example, fine-tuning a general language model on your company's documents and tone of voice so it writes in your style and understands your business context.
Frontier Model
The most advanced AI models available at any given time — the cutting edge of what's technically possible. GPT-4o, Claude 3.5 Sonnet, and Gemini Ultra are examples of frontier models. The term is used in AI policy and safety discussions to describe models powerful enough to potentially pose novel risks alongside their capabilities.
Foundation Model
A large AI model trained on a broad dataset that can be adapted for many different tasks. GPT-4, Claude, and Gemini are all foundation models. They're called 'foundation' models because they serve as the base that other, more specialized AI applications are built on top of.
Gemini
Google's flagship AI assistant and language model, available at gemini.google.com. Gemini integrates natively with Google Workspace — Gmail, Docs, Sheets, Drive — making it a strong choice for businesses already running on Google tools. It competes directly with ChatGPT and Claude and is available with a free tier and paid Google One AI Premium plan.
Grok
An AI assistant built by xAI, Elon Musk's AI company, and integrated into the X (formerly Twitter) platform. Grok is notable for having real-time access to information posted on X and for taking a less filtered approach to responses compared to some competitors. Available to X Premium subscribers and via the Grok standalone app.
Generative AI
AI that can create new content — text, images, audio, video, code — rather than just analysing or classifying existing content. ChatGPT generating a business proposal, DALL-E creating an image from a description, and GitHub Copilot writing code are all examples of generative AI in action.
GPT (Generative Pre-trained Transformer)
The type of AI model that powers ChatGPT, developed by OpenAI. GPT models are trained on vast amounts of text data and can generate human-like text, answer questions, summarize documents, write code, and much more. The 'transformer' part refers to the underlying architecture that makes modern AI so powerful.
Guardrails
Rules, filters, and safety mechanisms built into AI systems to prevent them from producing harmful, offensive, or inappropriate outputs. Guardrails are why AI assistants refuse certain requests. When building custom AI tools for businesses, we also add business-specific guardrails — for example, preventing a customer service bot from making promises the business can't keep.
Hallucination
When an AI confidently produces information that is factually incorrect or completely made up. This is one of the most important limitations of current AI tools to understand. AI doesn't 'know' things the way humans do — it predicts likely text, which can sometimes result in plausible-sounding but wrong answers. Always verify important facts.
Human-in-the-Loop
A system design where humans are involved in reviewing, approving, or correcting AI outputs before they're acted on. For most business applications, keeping a human in the loop is best practice — especially for anything customer-facing, financial, or legally sensitive.
Hyperparameter
Settings that control how an AI model is trained — like how fast it learns, how many training rounds it goes through, or how large its layers are. Unlike regular parameters (which the model learns automatically), hyperparameters are set by the people building the model. Getting them right is part of the craft of AI development.
Inference
The process of using a trained AI model to generate outputs from new inputs. When you type a question into ChatGPT and it responds, that's inference — the model is applying what it learned during training to your specific input. Training happens once; inference happens every time you use the tool.
Integration
Connecting two or more software systems so they can share data and work together. In AI, integration typically means connecting an AI tool to your existing business software — like linking a chatbot to your CRM, or connecting an automation tool to your booking system.
Jailbreak
An attempt to bypass the safety guidelines or restrictions built into an AI model — usually by crafting clever prompts that trick the AI into ignoring its rules. AI companies work continuously to prevent jailbreaks. As a business owner, it's worth knowing this exists so you understand why AI tools sometimes refuse certain requests — those guardrails are there for good reason.
Knowledge Base
A structured collection of information that an AI can draw on to answer questions. When we build a custom AI assistant for your business, we often create a knowledge base containing your FAQs, product details, policies, and procedures — so the AI gives accurate, business-specific answers rather than generic ones.
Large Language Model (LLM)
An AI model trained on enormous amounts of text data that can understand and generate human language. ChatGPT, Claude, and Gemini are all LLMs. They're called 'large' because of the sheer scale of data they're trained on and the number of parameters (internal settings) they contain — often in the billions.
Llama
Meta's family of open-source large language models, released for anyone to download, run, and build on. Unlike ChatGPT or Claude, Llama models can be run locally on your own hardware without sending data to a third-party server — making them popular for privacy-sensitive applications and businesses that want more control over their AI stack.
Machine Learning (ML)
A branch of AI where systems learn from data and improve their performance over time without being explicitly programmed for every scenario. Instead of following rigid rules, machine learning models identify patterns in data and use those patterns to make predictions or decisions.
Multimodal AI
AI that can process and generate multiple types of content — text, images, audio, and video — rather than just one. GPT-4o is multimodal: you can show it a photo and ask a question about it, or have it describe an image. Multimodal AI opens up a much wider range of business applications than text-only models.
Model
In AI, a model is the trained system that takes inputs and produces outputs. When people refer to 'the model', they mean the AI itself — the mathematical structure that has been trained on data. Different models have different strengths, weaknesses, and use cases.
Natural Language Processing (NLP)
The branch of AI focused on enabling computers to understand, interpret, and generate human language. NLP powers chatbots, voice assistants, translation tools, sentiment analysis, and much more. When an AI understands what you're asking in plain English, that's NLP at work.
Neural Network
A type of AI system loosely inspired by the structure of the human brain, made up of layers of interconnected nodes that process information. Neural networks are the foundation of most modern AI — including image recognition, speech recognition, and large language models.
No-Code / Low-Code
Tools and platforms that allow people to build software applications and automations without writing traditional code. Many AI tools are now available in no-code or low-code formats, making them accessible to business owners without technical backgrounds. Zapier and Make are popular examples.
OpenAI
The company behind ChatGPT, GPT-4, and DALL-E. OpenAI is one of the most influential AI research organizations in the world. Founded in 2015, it has become the most recognized name in consumer AI and provides the underlying technology for many third-party AI tools and applications.
Overfitting
When an AI model learns its training data too well — including the noise and quirks — and as a result performs poorly on new, real-world data. Think of it like a student who memorises past exam papers word-for-word but can't answer a question that's phrased slightly differently. Overfitting is a common challenge in building reliable AI systems.
Parameters
The internal settings of an AI model that are adjusted during training to improve performance. A model with more parameters is generally more capable but also more expensive to run. GPT-4 is estimated to have hundreds of billions of parameters — which is part of why it's so capable.
Prompt
The input you give to an AI model — the question, instruction, or context that tells the AI what you want it to do. Writing effective prompts (prompt engineering) is a skill that significantly affects the quality of AI outputs. A well-crafted prompt can be the difference between a mediocre and an excellent result.
Prompt Engineering
The practice of designing and refining prompts to get better outputs from AI models. It involves understanding how AI models interpret instructions and structuring your inputs to guide the model toward the response you want. Good prompt engineering is one of the most practical AI skills a business owner can develop.
Prompt Injection
A type of attack where a malicious user crafts inputs designed to override an AI system's instructions and make it behave in unintended ways. For example, a user might try to trick a customer service chatbot into ignoring its rules by embedding hidden instructions in their message. It's a real security concern for businesses deploying AI-facing tools — and something we account for when building AI systems for clients.
Query
A question or request submitted to an AI system, search engine, or database. In everyday AI use, a query is essentially what you type into ChatGPT or a search bar. In more technical contexts, queries are used to retrieve specific data from databases. The quality of your query directly affects the quality of the answer you get back.
RAG (Retrieval-Augmented Generation)
A technique that combines an AI language model with a search system, allowing the AI to retrieve relevant information from a specific knowledge base before generating a response. RAG is how you build AI tools that can answer questions about your specific business — your products, policies, FAQs — rather than just general knowledge.
Sentiment Analysis
An AI technique that identifies and categorises the emotional tone of text — positive, negative, or neutral. Businesses use sentiment analysis to monitor customer reviews, social media mentions, and support tickets to understand how customers feel about their products or services at scale.
Shadow AI
The use of AI tools by employees without the knowledge or approval of their organization's IT or management team. Just as 'shadow IT' described unauthorised software use, shadow AI is a growing concern — staff may be pasting sensitive client data into ChatGPT or using unapproved AI tools, creating data privacy and security risks. Having a clear AI policy in your business is increasingly important.
System Prompt
A set of instructions given to an AI model before a conversation begins, used to define its persona, rules, and behavior. When we build a custom AI chatbot for a business, the system prompt is where we tell it who it is, what it can and can't do, and how it should respond to customers.
Token
The basic unit of text that AI language models process. A token is roughly equivalent to a word or part of a word — 'hamburger' might be split into two tokens, while 'the' is one. AI models have token limits for inputs and outputs, and usage is often billed per token. Understanding tokens helps you use AI tools more efficiently.
Training
The process of teaching an AI model by exposing it to large amounts of data and adjusting its parameters to improve performance. Training a large AI model requires enormous computing power and can take weeks or months. Most businesses use pre-trained models rather than training their own from scratch.
Transformer
The underlying architecture that powers most modern AI language models, including GPT, Claude, and Gemini. Introduced by Google in 2017, the transformer architecture revolutionised AI by enabling models to process and understand context across long sequences of text far more effectively than previous approaches.
Use Case
A specific, practical application of a technology to solve a real problem. In AI, a use case might be 'using AI to automatically respond to common customer enquiries' or 'using AI to summarize meeting notes'. Identifying the right use cases for your business is the first step in any successful AI implementation.
Vector Database
A type of database designed to store and search data in the format that AI models use (vectors/embeddings). Vector databases are a key component of RAG systems — they allow AI tools to quickly find the most relevant information from a large knowledge base when answering a question.
Vibe Coding
A term coined in 2025 describing the practice of building software by describing what you want in plain English to an AI — and letting the AI write the code for you. Instead of learning to code, you 'vibe' with the AI: you describe the idea, review what it builds, give feedback, and iterate. Tools like Cursor, GitHub Copilot, and platforms like the one this website was built on make vibe coding accessible to non-developers. It's one of the most significant shifts in how software gets made.
Voice AI
AI systems that can understand and generate spoken language — enabling voice-based interactions with software. Voice AI powers smart speakers (Alexa, Siri, Google Assistant), AI phone receptionists, and voice-enabled customer service tools. For small businesses, voice AI can handle inbound calls, answer common questions, and route enquiries — 24 hours a day.
Workflow
A sequence of steps or tasks that make up a business process. In the context of AI and automation, a workflow is the series of actions that get triggered and executed automatically — for example, a new lead fills in a form, which triggers an AI to qualify them, send a welcome email, and add them to your CRM.
xAI
The AI company founded by Elon Musk in 2023, responsible for building Grok. xAI's stated mission is to understand the true nature of the universe through AI research. It's one of the newer major players in the AI space and is notable for its integration with the X (formerly Twitter) platform.
Yield (AI Output Quality)
In AI contexts, yield refers to the quality and usefulness of the outputs an AI produces relative to the inputs given. High-yield prompting means getting consistently valuable, accurate, and relevant responses. Improving your yield is largely about better prompts, clearer context, and choosing the right model for the task.
Zero-Shot Learning
The ability of an AI model to perform a task it hasn't been specifically trained on, using only a description or instruction. For example, asking ChatGPT to classify customer feedback into categories it's never seen before. Zero-shot capability is one of the things that makes modern LLMs so versatile for business use.
AI Washing
When a company exaggerates or falsely claims that its product uses AI to appear more innovative or attract investment. Similar to 'greenwashing' in sustainability. As AI becomes a buzzword, AI washing is increasingly common — it's worth asking vendors exactly how AI is used in their product, not just whether it is.
Algorithm
A set of step-by-step instructions a computer follows to complete a task or solve a problem. Think of it like a recipe — the computer follows each step in order to produce a result. Most AI tools are built on complex algorithms that process data and generate outputs.
Artificial Intelligence (AI)
Technology that enables computers to perform tasks that normally require human intelligence — things like understanding language, recognizing images, making decisions, and learning from experience. AI is an umbrella term that covers many different technologies and approaches.
Automation
Using technology to perform tasks with little or no human input. In a business context, automation means setting up systems that handle repetitive work automatically — like sending follow-up emails, updating spreadsheets, or routing customer enquiries — so your team can focus on higher-value work.
Agentic AI
AI that can take actions and make decisions autonomously to complete a goal — not just answer questions, but actually do things like browse the web, send emails, or interact with software on your behalf. Agentic AI is one of the fastest-growing areas of AI development.
AI-Native
A product, business, or workflow that is built from the ground up with AI at its core — rather than having AI bolted on as an afterthought. An AI-native business uses AI as a fundamental part of how it operates, not just as an occasional tool. Increasingly, being AI-native is a competitive advantage.
API (Application Programming Interface)
A way for two software applications to talk to each other. When we integrate an AI tool with your existing software, we're usually using APIs to connect them. You don't need to understand how APIs work — just know they're the plumbing that makes integrations possible.
ChatGPT
The AI assistant built by OpenAI and one of the most widely used AI tools in the world. ChatGPT can write, summarize, answer questions, draft emails, generate ideas, and much more — all through a simple chat interface. It runs on OpenAI's GPT models and is available free or via a paid plan. For most small businesses, it's the best starting point for exploring what AI can do.
Claude
An AI assistant built by Anthropic, widely regarded as one of the best alternatives to ChatGPT. Claude is known for nuanced writing, long-document analysis, and following complex instructions carefully. It's designed with a strong focus on safety and honesty. Available at claude.ai with a free tier and paid plans.
Bot
Short for 'robot' — a software program that performs automated tasks. In business, bots are commonly used for customer service (chatbots), data collection, and repetitive admin tasks. Not all bots use AI, but AI-powered bots are far more capable than simple rule-based ones.
Benchmark
A standardised test used to measure and compare the performance of AI models. When you see claims like 'our AI scores 90% on X benchmark', it means the model was tested against a standard set of problems. Benchmarks help compare different AI tools, though real-world performance can vary.
Black Box
A system where you can see the inputs and outputs but not what happens in between. Most AI models are black boxes — you give them a prompt and get a response, but the internal reasoning is not visible or easily explainable. This is why 'explainable AI' is an active area of research, particularly in regulated industries like finance and healthcare.
Chatbot
A software program designed to simulate conversation with humans, typically via text. Modern AI-powered chatbots (like those built on ChatGPT) can understand natural language, answer complex questions, and handle customer service tasks around the clock — far beyond the simple 'press 1 for billing' bots of the past.
Context Window
The amount of text an AI model can 'see' and consider at one time. Think of it like short-term memory — the larger the context window, the more of a conversation or document the AI can take into account when generating a response. Larger context windows generally produce more coherent, relevant outputs.
Copilot
An AI assistant that works alongside a human to help them do their job better — not replacing them, but augmenting their capabilities. Microsoft's Copilot (built into Office 365) is a well-known example. The term reflects the idea that the human is still in control; the AI is just helping navigate.
Data
Information in a form that computers can process. AI systems learn from data — the more relevant, high-quality data an AI is trained on, the better it tends to perform. In a business context, your data includes things like customer records, transaction history, emails, and documents.
Deepfake
AI-generated video, audio, or images that realistically depict someone saying or doing something they never actually said or did. Deepfakes are created using deep learning techniques and have become increasingly convincing. As a business owner, it's important to be aware of deepfakes — both as a potential security risk (e.g. fake audio of a CEO authorising a transfer) and as a reputational concern.
Deep Learning
A type of machine learning that uses neural networks with many layers to learn from large amounts of data. Deep learning powers many of the most impressive AI capabilities today — including image recognition, speech recognition, and large language models like ChatGPT.
Embedding
A way of representing text, images, or other data as numbers so that an AI can understand relationships between concepts. Embeddings allow AI to understand that 'dog' and 'puppy' are related, or that 'invoice' and 'bill' mean similar things — even without being explicitly told.
Emergent Behavior
Unexpected capabilities that appear in AI models as they grow larger or are trained on more data — abilities that weren't explicitly programmed or anticipated. For example, large language models spontaneously developed the ability to do basic arithmetic and translate languages without being specifically trained to do so. Emergent behavior is one of the reasons AI development can be unpredictable.
Fine-Tuning
The process of taking a pre-trained AI model and training it further on a specific dataset to make it better at a particular task. For example, fine-tuning a general language model on your company's documents and tone of voice so it writes in your style and understands your business context.
Frontier Model
The most advanced AI models available at any given time — the cutting edge of what's technically possible. GPT-4o, Claude 3.5 Sonnet, and Gemini Ultra are examples of frontier models. The term is used in AI policy and safety discussions to describe models powerful enough to potentially pose novel risks alongside their capabilities.
Foundation Model
A large AI model trained on a broad dataset that can be adapted for many different tasks. GPT-4, Claude, and Gemini are all foundation models. They're called 'foundation' models because they serve as the base that other, more specialized AI applications are built on top of.
Gemini
Google's flagship AI assistant and language model, available at gemini.google.com. Gemini integrates natively with Google Workspace — Gmail, Docs, Sheets, Drive — making it a strong choice for businesses already running on Google tools. It competes directly with ChatGPT and Claude and is available with a free tier and paid Google One AI Premium plan.
Grok
An AI assistant built by xAI, Elon Musk's AI company, and integrated into the X (formerly Twitter) platform. Grok is notable for having real-time access to information posted on X and for taking a less filtered approach to responses compared to some competitors. Available to X Premium subscribers and via the Grok standalone app.
Generative AI
AI that can create new content — text, images, audio, video, code — rather than just analysing or classifying existing content. ChatGPT generating a business proposal, DALL-E creating an image from a description, and GitHub Copilot writing code are all examples of generative AI in action.
GPT (Generative Pre-trained Transformer)
The type of AI model that powers ChatGPT, developed by OpenAI. GPT models are trained on vast amounts of text data and can generate human-like text, answer questions, summarize documents, write code, and much more. The 'transformer' part refers to the underlying architecture that makes modern AI so powerful.
Guardrails
Rules, filters, and safety mechanisms built into AI systems to prevent them from producing harmful, offensive, or inappropriate outputs. Guardrails are why AI assistants refuse certain requests. When building custom AI tools for businesses, we also add business-specific guardrails — for example, preventing a customer service bot from making promises the business can't keep.
Hallucination
When an AI confidently produces information that is factually incorrect or completely made up. This is one of the most important limitations of current AI tools to understand. AI doesn't 'know' things the way humans do — it predicts likely text, which can sometimes result in plausible-sounding but wrong answers. Always verify important facts.
Human-in-the-Loop
A system design where humans are involved in reviewing, approving, or correcting AI outputs before they're acted on. For most business applications, keeping a human in the loop is best practice — especially for anything customer-facing, financial, or legally sensitive.
Hyperparameter
Settings that control how an AI model is trained — like how fast it learns, how many training rounds it goes through, or how large its layers are. Unlike regular parameters (which the model learns automatically), hyperparameters are set by the people building the model. Getting them right is part of the craft of AI development.
Inference
The process of using a trained AI model to generate outputs from new inputs. When you type a question into ChatGPT and it responds, that's inference — the model is applying what it learned during training to your specific input. Training happens once; inference happens every time you use the tool.
Integration
Connecting two or more software systems so they can share data and work together. In AI, integration typically means connecting an AI tool to your existing business software — like linking a chatbot to your CRM, or connecting an automation tool to your booking system.
Jailbreak
An attempt to bypass the safety guidelines or restrictions built into an AI model — usually by crafting clever prompts that trick the AI into ignoring its rules. AI companies work continuously to prevent jailbreaks. As a business owner, it's worth knowing this exists so you understand why AI tools sometimes refuse certain requests — those guardrails are there for good reason.
Knowledge Base
A structured collection of information that an AI can draw on to answer questions. When we build a custom AI assistant for your business, we often create a knowledge base containing your FAQs, product details, policies, and procedures — so the AI gives accurate, business-specific answers rather than generic ones.
Large Language Model (LLM)
An AI model trained on enormous amounts of text data that can understand and generate human language. ChatGPT, Claude, and Gemini are all LLMs. They're called 'large' because of the sheer scale of data they're trained on and the number of parameters (internal settings) they contain — often in the billions.
Llama
Meta's family of open-source large language models, released for anyone to download, run, and build on. Unlike ChatGPT or Claude, Llama models can be run locally on your own hardware without sending data to a third-party server — making them popular for privacy-sensitive applications and businesses that want more control over their AI stack.
Machine Learning (ML)
A branch of AI where systems learn from data and improve their performance over time without being explicitly programmed for every scenario. Instead of following rigid rules, machine learning models identify patterns in data and use those patterns to make predictions or decisions.
Multimodal AI
AI that can process and generate multiple types of content — text, images, audio, and video — rather than just one. GPT-4o is multimodal: you can show it a photo and ask a question about it, or have it describe an image. Multimodal AI opens up a much wider range of business applications than text-only models.
Model
In AI, a model is the trained system that takes inputs and produces outputs. When people refer to 'the model', they mean the AI itself — the mathematical structure that has been trained on data. Different models have different strengths, weaknesses, and use cases.
Natural Language Processing (NLP)
The branch of AI focused on enabling computers to understand, interpret, and generate human language. NLP powers chatbots, voice assistants, translation tools, sentiment analysis, and much more. When an AI understands what you're asking in plain English, that's NLP at work.
Neural Network
A type of AI system loosely inspired by the structure of the human brain, made up of layers of interconnected nodes that process information. Neural networks are the foundation of most modern AI — including image recognition, speech recognition, and large language models.
No-Code / Low-Code
Tools and platforms that allow people to build software applications and automations without writing traditional code. Many AI tools are now available in no-code or low-code formats, making them accessible to business owners without technical backgrounds. Zapier and Make are popular examples.
OpenAI
The company behind ChatGPT, GPT-4, and DALL-E. OpenAI is one of the most influential AI research organizations in the world. Founded in 2015, it has become the most recognized name in consumer AI and provides the underlying technology for many third-party AI tools and applications.
Overfitting
When an AI model learns its training data too well — including the noise and quirks — and as a result performs poorly on new, real-world data. Think of it like a student who memorises past exam papers word-for-word but can't answer a question that's phrased slightly differently. Overfitting is a common challenge in building reliable AI systems.
Parameters
The internal settings of an AI model that are adjusted during training to improve performance. A model with more parameters is generally more capable but also more expensive to run. GPT-4 is estimated to have hundreds of billions of parameters — which is part of why it's so capable.
Prompt
The input you give to an AI model — the question, instruction, or context that tells the AI what you want it to do. Writing effective prompts (prompt engineering) is a skill that significantly affects the quality of AI outputs. A well-crafted prompt can be the difference between a mediocre and an excellent result.
Prompt Engineering
The practice of designing and refining prompts to get better outputs from AI models. It involves understanding how AI models interpret instructions and structuring your inputs to guide the model toward the response you want. Good prompt engineering is one of the most practical AI skills a business owner can develop.
Prompt Injection
A type of attack where a malicious user crafts inputs designed to override an AI system's instructions and make it behave in unintended ways. For example, a user might try to trick a customer service chatbot into ignoring its rules by embedding hidden instructions in their message. It's a real security concern for businesses deploying AI-facing tools — and something we account for when building AI systems for clients.
Query
A question or request submitted to an AI system, search engine, or database. In everyday AI use, a query is essentially what you type into ChatGPT or a search bar. In more technical contexts, queries are used to retrieve specific data from databases. The quality of your query directly affects the quality of the answer you get back.
RAG (Retrieval-Augmented Generation)
A technique that combines an AI language model with a search system, allowing the AI to retrieve relevant information from a specific knowledge base before generating a response. RAG is how you build AI tools that can answer questions about your specific business — your products, policies, FAQs — rather than just general knowledge.
Sentiment Analysis
An AI technique that identifies and categorises the emotional tone of text — positive, negative, or neutral. Businesses use sentiment analysis to monitor customer reviews, social media mentions, and support tickets to understand how customers feel about their products or services at scale.
Shadow AI
The use of AI tools by employees without the knowledge or approval of their organization's IT or management team. Just as 'shadow IT' described unauthorised software use, shadow AI is a growing concern — staff may be pasting sensitive client data into ChatGPT or using unapproved AI tools, creating data privacy and security risks. Having a clear AI policy in your business is increasingly important.
System Prompt
A set of instructions given to an AI model before a conversation begins, used to define its persona, rules, and behavior. When we build a custom AI chatbot for a business, the system prompt is where we tell it who it is, what it can and can't do, and how it should respond to customers.
Token
The basic unit of text that AI language models process. A token is roughly equivalent to a word or part of a word — 'hamburger' might be split into two tokens, while 'the' is one. AI models have token limits for inputs and outputs, and usage is often billed per token. Understanding tokens helps you use AI tools more efficiently.
Training
The process of teaching an AI model by exposing it to large amounts of data and adjusting its parameters to improve performance. Training a large AI model requires enormous computing power and can take weeks or months. Most businesses use pre-trained models rather than training their own from scratch.
Transformer
The underlying architecture that powers most modern AI language models, including GPT, Claude, and Gemini. Introduced by Google in 2017, the transformer architecture revolutionised AI by enabling models to process and understand context across long sequences of text far more effectively than previous approaches.
Use Case
A specific, practical application of a technology to solve a real problem. In AI, a use case might be 'using AI to automatically respond to common customer enquiries' or 'using AI to summarize meeting notes'. Identifying the right use cases for your business is the first step in any successful AI implementation.
Vector Database
A type of database designed to store and search data in the format that AI models use (vectors/embeddings). Vector databases are a key component of RAG systems — they allow AI tools to quickly find the most relevant information from a large knowledge base when answering a question.
Vibe Coding
A term coined in 2025 describing the practice of building software by describing what you want in plain English to an AI — and letting the AI write the code for you. Instead of learning to code, you 'vibe' with the AI: you describe the idea, review what it builds, give feedback, and iterate. Tools like Cursor, GitHub Copilot, and platforms like the one this website was built on make vibe coding accessible to non-developers. It's one of the most significant shifts in how software gets made.
Voice AI
AI systems that can understand and generate spoken language — enabling voice-based interactions with software. Voice AI powers smart speakers (Alexa, Siri, Google Assistant), AI phone receptionists, and voice-enabled customer service tools. For small businesses, voice AI can handle inbound calls, answer common questions, and route enquiries — 24 hours a day.
Workflow
A sequence of steps or tasks that make up a business process. In the context of AI and automation, a workflow is the series of actions that get triggered and executed automatically — for example, a new lead fills in a form, which triggers an AI to qualify them, send a welcome email, and add them to your CRM.
xAI
The AI company founded by Elon Musk in 2023, responsible for building Grok. xAI's stated mission is to understand the true nature of the universe through AI research. It's one of the newer major players in the AI space and is notable for its integration with the X (formerly Twitter) platform.
Yield (AI Output Quality)
In AI contexts, yield refers to the quality and usefulness of the outputs an AI produces relative to the inputs given. High-yield prompting means getting consistently valuable, accurate, and relevant responses. Improving your yield is largely about better prompts, clearer context, and choosing the right model for the task.
Zero-Shot Learning
The ability of an AI model to perform a task it hasn't been specifically trained on, using only a description or instruction. For example, asking ChatGPT to classify customer feedback into categories it's never seen before. Zero-shot capability is one of the things that makes modern LLMs so versatile for business use.
AI Washing
When a company exaggerates or falsely claims that its product uses AI to appear more innovative or attract investment. Similar to 'greenwashing' in sustainability. As AI becomes a buzzword, AI washing is increasingly common — it's worth asking vendors exactly how AI is used in their product, not just whether it is.
Algorithm
A set of step-by-step instructions a computer follows to complete a task or solve a problem. Think of it like a recipe — the computer follows each step in order to produce a result. Most AI tools are built on complex algorithms that process data and generate outputs.
Artificial Intelligence (AI)
Technology that enables computers to perform tasks that normally require human intelligence — things like understanding language, recognizing images, making decisions, and learning from experience. AI is an umbrella term that covers many different technologies and approaches.
Automation
Using technology to perform tasks with little or no human input. In a business context, automation means setting up systems that handle repetitive work automatically — like sending follow-up emails, updating spreadsheets, or routing customer enquiries — so your team can focus on higher-value work.
Agentic AI
AI that can take actions and make decisions autonomously to complete a goal — not just answer questions, but actually do things like browse the web, send emails, or interact with software on your behalf. Agentic AI is one of the fastest-growing areas of AI development.
AI-Native
A product, business, or workflow that is built from the ground up with AI at its core — rather than having AI bolted on as an afterthought. An AI-native business uses AI as a fundamental part of how it operates, not just as an occasional tool. Increasingly, being AI-native is a competitive advantage.
API (Application Programming Interface)
A way for two software applications to talk to each other. When we integrate an AI tool with your existing software, we're usually using APIs to connect them. You don't need to understand how APIs work — just know they're the plumbing that makes integrations possible.
ChatGPT
The AI assistant built by OpenAI and one of the most widely used AI tools in the world. ChatGPT can write, summarize, answer questions, draft emails, generate ideas, and much more — all through a simple chat interface. It runs on OpenAI's GPT models and is available free or via a paid plan. For most small businesses, it's the best starting point for exploring what AI can do.
Claude
An AI assistant built by Anthropic, widely regarded as one of the best alternatives to ChatGPT. Claude is known for nuanced writing, long-document analysis, and following complex instructions carefully. It's designed with a strong focus on safety and honesty. Available at claude.ai with a free tier and paid plans.
Bot
Short for 'robot' — a software program that performs automated tasks. In business, bots are commonly used for customer service (chatbots), data collection, and repetitive admin tasks. Not all bots use AI, but AI-powered bots are far more capable than simple rule-based ones.
Benchmark
A standardised test used to measure and compare the performance of AI models. When you see claims like 'our AI scores 90% on X benchmark', it means the model was tested against a standard set of problems. Benchmarks help compare different AI tools, though real-world performance can vary.
Black Box
A system where you can see the inputs and outputs but not what happens in between. Most AI models are black boxes — you give them a prompt and get a response, but the internal reasoning is not visible or easily explainable. This is why 'explainable AI' is an active area of research, particularly in regulated industries like finance and healthcare.
Chatbot
A software program designed to simulate conversation with humans, typically via text. Modern AI-powered chatbots (like those built on ChatGPT) can understand natural language, answer complex questions, and handle customer service tasks around the clock — far beyond the simple 'press 1 for billing' bots of the past.
Context Window
The amount of text an AI model can 'see' and consider at one time. Think of it like short-term memory — the larger the context window, the more of a conversation or document the AI can take into account when generating a response. Larger context windows generally produce more coherent, relevant outputs.
Copilot
An AI assistant that works alongside a human to help them do their job better — not replacing them, but augmenting their capabilities. Microsoft's Copilot (built into Office 365) is a well-known example. The term reflects the idea that the human is still in control; the AI is just helping navigate.
Data
Information in a form that computers can process. AI systems learn from data — the more relevant, high-quality data an AI is trained on, the better it tends to perform. In a business context, your data includes things like customer records, transaction history, emails, and documents.
Deepfake
AI-generated video, audio, or images that realistically depict someone saying or doing something they never actually said or did. Deepfakes are created using deep learning techniques and have become increasingly convincing. As a business owner, it's important to be aware of deepfakes — both as a potential security risk (e.g. fake audio of a CEO authorising a transfer) and as a reputational concern.
Deep Learning
A type of machine learning that uses neural networks with many layers to learn from large amounts of data. Deep learning powers many of the most impressive AI capabilities today — including image recognition, speech recognition, and large language models like ChatGPT.
Embedding
A way of representing text, images, or other data as numbers so that an AI can understand relationships between concepts. Embeddings allow AI to understand that 'dog' and 'puppy' are related, or that 'invoice' and 'bill' mean similar things — even without being explicitly told.
Emergent Behavior
Unexpected capabilities that appear in AI models as they grow larger or are trained on more data — abilities that weren't explicitly programmed or anticipated. For example, large language models spontaneously developed the ability to do basic arithmetic and translate languages without being specifically trained to do so. Emergent behavior is one of the reasons AI development can be unpredictable.
Fine-Tuning
The process of taking a pre-trained AI model and training it further on a specific dataset to make it better at a particular task. For example, fine-tuning a general language model on your company's documents and tone of voice so it writes in your style and understands your business context.
Frontier Model
The most advanced AI models available at any given time — the cutting edge of what's technically possible. GPT-4o, Claude 3.5 Sonnet, and Gemini Ultra are examples of frontier models. The term is used in AI policy and safety discussions to describe models powerful enough to potentially pose novel risks alongside their capabilities.
Foundation Model
A large AI model trained on a broad dataset that can be adapted for many different tasks. GPT-4, Claude, and Gemini are all foundation models. They're called 'foundation' models because they serve as the base that other, more specialized AI applications are built on top of.
Gemini
Google's flagship AI assistant and language model, available at gemini.google.com. Gemini integrates natively with Google Workspace — Gmail, Docs, Sheets, Drive — making it a strong choice for businesses already running on Google tools. It competes directly with ChatGPT and Claude and is available with a free tier and paid Google One AI Premium plan.
Grok
An AI assistant built by xAI, Elon Musk's AI company, and integrated into the X (formerly Twitter) platform. Grok is notable for having real-time access to information posted on X and for taking a less filtered approach to responses compared to some competitors. Available to X Premium subscribers and via the Grok standalone app.
Generative AI
AI that can create new content — text, images, audio, video, code — rather than just analysing or classifying existing content. ChatGPT generating a business proposal, DALL-E creating an image from a description, and GitHub Copilot writing code are all examples of generative AI in action.
GPT (Generative Pre-trained Transformer)
The type of AI model that powers ChatGPT, developed by OpenAI. GPT models are trained on vast amounts of text data and can generate human-like text, answer questions, summarize documents, write code, and much more. The 'transformer' part refers to the underlying architecture that makes modern AI so powerful.
Guardrails
Rules, filters, and safety mechanisms built into AI systems to prevent them from producing harmful, offensive, or inappropriate outputs. Guardrails are why AI assistants refuse certain requests. When building custom AI tools for businesses, we also add business-specific guardrails — for example, preventing a customer service bot from making promises the business can't keep.
Hallucination
When an AI confidently produces information that is factually incorrect or completely made up. This is one of the most important limitations of current AI tools to understand. AI doesn't 'know' things the way humans do — it predicts likely text, which can sometimes result in plausible-sounding but wrong answers. Always verify important facts.
Human-in-the-Loop
A system design where humans are involved in reviewing, approving, or correcting AI outputs before they're acted on. For most business applications, keeping a human in the loop is best practice — especially for anything customer-facing, financial, or legally sensitive.
Hyperparameter
Settings that control how an AI model is trained — like how fast it learns, how many training rounds it goes through, or how large its layers are. Unlike regular parameters (which the model learns automatically), hyperparameters are set by the people building the model. Getting them right is part of the craft of AI development.
Inference
The process of using a trained AI model to generate outputs from new inputs. When you type a question into ChatGPT and it responds, that's inference — the model is applying what it learned during training to your specific input. Training happens once; inference happens every time you use the tool.
Integration
Connecting two or more software systems so they can share data and work together. In AI, integration typically means connecting an AI tool to your existing business software — like linking a chatbot to your CRM, or connecting an automation tool to your booking system.
Jailbreak
An attempt to bypass the safety guidelines or restrictions built into an AI model — usually by crafting clever prompts that trick the AI into ignoring its rules. AI companies work continuously to prevent jailbreaks. As a business owner, it's worth knowing this exists so you understand why AI tools sometimes refuse certain requests — those guardrails are there for good reason.
Knowledge Base
A structured collection of information that an AI can draw on to answer questions. When we build a custom AI assistant for your business, we often create a knowledge base containing your FAQs, product details, policies, and procedures — so the AI gives accurate, business-specific answers rather than generic ones.
Large Language Model (LLM)
An AI model trained on enormous amounts of text data that can understand and generate human language. ChatGPT, Claude, and Gemini are all LLMs. They're called 'large' because of the sheer scale of data they're trained on and the number of parameters (internal settings) they contain — often in the billions.
Llama
Meta's family of open-source large language models, released for anyone to download, run, and build on. Unlike ChatGPT or Claude, Llama models can be run locally on your own hardware without sending data to a third-party server — making them popular for privacy-sensitive applications and businesses that want more control over their AI stack.
Machine Learning (ML)
A branch of AI where systems learn from data and improve their performance over time without being explicitly programmed for every scenario. Instead of following rigid rules, machine learning models identify patterns in data and use those patterns to make predictions or decisions.
Multimodal AI
AI that can process and generate multiple types of content — text, images, audio, and video — rather than just one. GPT-4o is multimodal: you can show it a photo and ask a question about it, or have it describe an image. Multimodal AI opens up a much wider range of business applications than text-only models.
Model
In AI, a model is the trained system that takes inputs and produces outputs. When people refer to 'the model', they mean the AI itself — the mathematical structure that has been trained on data. Different models have different strengths, weaknesses, and use cases.
Natural Language Processing (NLP)
The branch of AI focused on enabling computers to understand, interpret, and generate human language. NLP powers chatbots, voice assistants, translation tools, sentiment analysis, and much more. When an AI understands what you're asking in plain English, that's NLP at work.
Neural Network
A type of AI system loosely inspired by the structure of the human brain, made up of layers of interconnected nodes that process information. Neural networks are the foundation of most modern AI — including image recognition, speech recognition, and large language models.
No-Code / Low-Code
Tools and platforms that allow people to build software applications and automations without writing traditional code. Many AI tools are now available in no-code or low-code formats, making them accessible to business owners without technical backgrounds. Zapier and Make are popular examples.
OpenAI
The company behind ChatGPT, GPT-4, and DALL-E. OpenAI is one of the most influential AI research organizations in the world. Founded in 2015, it has become the most recognized name in consumer AI and provides the underlying technology for many third-party AI tools and applications.
Overfitting
When an AI model learns its training data too well — including the noise and quirks — and as a result performs poorly on new, real-world data. Think of it like a student who memorises past exam papers word-for-word but can't answer a question that's phrased slightly differently. Overfitting is a common challenge in building reliable AI systems.
Parameters
The internal settings of an AI model that are adjusted during training to improve performance. A model with more parameters is generally more capable but also more expensive to run. GPT-4 is estimated to have hundreds of billions of parameters — which is part of why it's so capable.
Prompt
The input you give to an AI model — the question, instruction, or context that tells the AI what you want it to do. Writing effective prompts (prompt engineering) is a skill that significantly affects the quality of AI outputs. A well-crafted prompt can be the difference between a mediocre and an excellent result.
Prompt Engineering
The practice of designing and refining prompts to get better outputs from AI models. It involves understanding how AI models interpret instructions and structuring your inputs to guide the model toward the response you want. Good prompt engineering is one of the most practical AI skills a business owner can develop.
Prompt Injection
A type of attack where a malicious user crafts inputs designed to override an AI system's instructions and make it behave in unintended ways. For example, a user might try to trick a customer service chatbot into ignoring its rules by embedding hidden instructions in their message. It's a real security concern for businesses deploying AI-facing tools — and something we account for when building AI systems for clients.
Query
A question or request submitted to an AI system, search engine, or database. In everyday AI use, a query is essentially what you type into ChatGPT or a search bar. In more technical contexts, queries are used to retrieve specific data from databases. The quality of your query directly affects the quality of the answer you get back.
RAG (Retrieval-Augmented Generation)
A technique that combines an AI language model with a search system, allowing the AI to retrieve relevant information from a specific knowledge base before generating a response. RAG is how you build AI tools that can answer questions about your specific business — your products, policies, FAQs — rather than just general knowledge.
Sentiment Analysis
An AI technique that identifies and categorises the emotional tone of text — positive, negative, or neutral. Businesses use sentiment analysis to monitor customer reviews, social media mentions, and support tickets to understand how customers feel about their products or services at scale.
Shadow AI
The use of AI tools by employees without the knowledge or approval of their organization's IT or management team. Just as 'shadow IT' described unauthorised software use, shadow AI is a growing concern — staff may be pasting sensitive client data into ChatGPT or using unapproved AI tools, creating data privacy and security risks. Having a clear AI policy in your business is increasingly important.
System Prompt
A set of instructions given to an AI model before a conversation begins, used to define its persona, rules, and behavior. When we build a custom AI chatbot for a business, the system prompt is where we tell it who it is, what it can and can't do, and how it should respond to customers.
Token
The basic unit of text that AI language models process. A token is roughly equivalent to a word or part of a word — 'hamburger' might be split into two tokens, while 'the' is one. AI models have token limits for inputs and outputs, and usage is often billed per token. Understanding tokens helps you use AI tools more efficiently.
Training
The process of teaching an AI model by exposing it to large amounts of data and adjusting its parameters to improve performance. Training a large AI model requires enormous computing power and can take weeks or months. Most businesses use pre-trained models rather than training their own from scratch.
Transformer
The underlying architecture that powers most modern AI language models, including GPT, Claude, and Gemini. Introduced by Google in 2017, the transformer architecture revolutionised AI by enabling models to process and understand context across long sequences of text far more effectively than previous approaches.
Use Case
A specific, practical application of a technology to solve a real problem. In AI, a use case might be 'using AI to automatically respond to common customer enquiries' or 'using AI to summarize meeting notes'. Identifying the right use cases for your business is the first step in any successful AI implementation.
Vector Database
A type of database designed to store and search data in the format that AI models use (vectors/embeddings). Vector databases are a key component of RAG systems — they allow AI tools to quickly find the most relevant information from a large knowledge base when answering a question.
Vibe Coding
A term coined in 2025 describing the practice of building software by describing what you want in plain English to an AI — and letting the AI write the code for you. Instead of learning to code, you 'vibe' with the AI: you describe the idea, review what it builds, give feedback, and iterate. Tools like Cursor, GitHub Copilot, and platforms like the one this website was built on make vibe coding accessible to non-developers. It's one of the most significant shifts in how software gets made.
Voice AI
AI systems that can understand and generate spoken language — enabling voice-based interactions with software. Voice AI powers smart speakers (Alexa, Siri, Google Assistant), AI phone receptionists, and voice-enabled customer service tools. For small businesses, voice AI can handle inbound calls, answer common questions, and route enquiries — 24 hours a day.
Workflow
A sequence of steps or tasks that make up a business process. In the context of AI and automation, a workflow is the series of actions that get triggered and executed automatically — for example, a new lead fills in a form, which triggers an AI to qualify them, send a welcome email, and add them to your CRM.
xAI
The AI company founded by Elon Musk in 2023, responsible for building Grok. xAI's stated mission is to understand the true nature of the universe through AI research. It's one of the newer major players in the AI space and is notable for its integration with the X (formerly Twitter) platform.
Yield (AI Output Quality)
In AI contexts, yield refers to the quality and usefulness of the outputs an AI produces relative to the inputs given. High-yield prompting means getting consistently valuable, accurate, and relevant responses. Improving your yield is largely about better prompts, clearer context, and choosing the right model for the task.
Zero-Shot Learning
The ability of an AI model to perform a task it hasn't been specifically trained on, using only a description or instruction. For example, asking ChatGPT to classify customer feedback into categories it's never seen before. Zero-shot capability is one of the things that makes modern LLMs so versatile for business use.
AI Washing
When a company exaggerates or falsely claims that its product uses AI to appear more innovative or attract investment. Similar to 'greenwashing' in sustainability. As AI becomes a buzzword, AI washing is increasingly common — it's worth asking vendors exactly how AI is used in their product, not just whether it is.
Algorithm
A set of step-by-step instructions a computer follows to complete a task or solve a problem. Think of it like a recipe — the computer follows each step in order to produce a result. Most AI tools are built on complex algorithms that process data and generate outputs.
Artificial Intelligence (AI)
Technology that enables computers to perform tasks that normally require human intelligence — things like understanding language, recognizing images, making decisions, and learning from experience. AI is an umbrella term that covers many different technologies and approaches.
Automation
Using technology to perform tasks with little or no human input. In a business context, automation means setting up systems that handle repetitive work automatically — like sending follow-up emails, updating spreadsheets, or routing customer enquiries — so your team can focus on higher-value work.
Agentic AI
AI that can take actions and make decisions autonomously to complete a goal — not just answer questions, but actually do things like browse the web, send emails, or interact with software on your behalf. Agentic AI is one of the fastest-growing areas of AI development.
AI-Native
A product, business, or workflow that is built from the ground up with AI at its core — rather than having AI bolted on as an afterthought. An AI-native business uses AI as a fundamental part of how it operates, not just as an occasional tool. Increasingly, being AI-native is a competitive advantage.
API (Application Programming Interface)
A way for two software applications to talk to each other. When we integrate an AI tool with your existing software, we're usually using APIs to connect them. You don't need to understand how APIs work — just know they're the plumbing that makes integrations possible.
ChatGPT
The AI assistant built by OpenAI and one of the most widely used AI tools in the world. ChatGPT can write, summarize, answer questions, draft emails, generate ideas, and much more — all through a simple chat interface. It runs on OpenAI's GPT models and is available free or via a paid plan. For most small businesses, it's the best starting point for exploring what AI can do.
Claude
An AI assistant built by Anthropic, widely regarded as one of the best alternatives to ChatGPT. Claude is known for nuanced writing, long-document analysis, and following complex instructions carefully. It's designed with a strong focus on safety and honesty. Available at claude.ai with a free tier and paid plans.
Bot
Short for 'robot' — a software program that performs automated tasks. In business, bots are commonly used for customer service (chatbots), data collection, and repetitive admin tasks. Not all bots use AI, but AI-powered bots are far more capable than simple rule-based ones.
Benchmark
A standardised test used to measure and compare the performance of AI models. When you see claims like 'our AI scores 90% on X benchmark', it means the model was tested against a standard set of problems. Benchmarks help compare different AI tools, though real-world performance can vary.
Black Box
A system where you can see the inputs and outputs but not what happens in between. Most AI models are black boxes — you give them a prompt and get a response, but the internal reasoning is not visible or easily explainable. This is why 'explainable AI' is an active area of research, particularly in regulated industries like finance and healthcare.
Chatbot
A software program designed to simulate conversation with humans, typically via text. Modern AI-powered chatbots (like those built on ChatGPT) can understand natural language, answer complex questions, and handle customer service tasks around the clock — far beyond the simple 'press 1 for billing' bots of the past.
Context Window
The amount of text an AI model can 'see' and consider at one time. Think of it like short-term memory — the larger the context window, the more of a conversation or document the AI can take into account when generating a response. Larger context windows generally produce more coherent, relevant outputs.
Copilot
An AI assistant that works alongside a human to help them do their job better — not replacing them, but augmenting their capabilities. Microsoft's Copilot (built into Office 365) is a well-known example. The term reflects the idea that the human is still in control; the AI is just helping navigate.
Data
Information in a form that computers can process. AI systems learn from data — the more relevant, high-quality data an AI is trained on, the better it tends to perform. In a business context, your data includes things like customer records, transaction history, emails, and documents.
Deepfake
AI-generated video, audio, or images that realistically depict someone saying or doing something they never actually said or did. Deepfakes are created using deep learning techniques and have become increasingly convincing. As a business owner, it's important to be aware of deepfakes — both as a potential security risk (e.g. fake audio of a CEO authorising a transfer) and as a reputational concern.
Deep Learning
A type of machine learning that uses neural networks with many layers to learn from large amounts of data. Deep learning powers many of the most impressive AI capabilities today — including image recognition, speech recognition, and large language models like ChatGPT.
Embedding
A way of representing text, images, or other data as numbers so that an AI can understand relationships between concepts. Embeddings allow AI to understand that 'dog' and 'puppy' are related, or that 'invoice' and 'bill' mean similar things — even without being explicitly told.
Emergent Behavior
Unexpected capabilities that appear in AI models as they grow larger or are trained on more data — abilities that weren't explicitly programmed or anticipated. For example, large language models spontaneously developed the ability to do basic arithmetic and translate languages without being specifically trained to do so. Emergent behavior is one of the reasons AI development can be unpredictable.
Fine-Tuning
The process of taking a pre-trained AI model and training it further on a specific dataset to make it better at a particular task. For example, fine-tuning a general language model on your company's documents and tone of voice so it writes in your style and understands your business context.
Frontier Model
The most advanced AI models available at any given time — the cutting edge of what's technically possible. GPT-4o, Claude 3.5 Sonnet, and Gemini Ultra are examples of frontier models. The term is used in AI policy and safety discussions to describe models powerful enough to potentially pose novel risks alongside their capabilities.
Foundation Model
A large AI model trained on a broad dataset that can be adapted for many different tasks. GPT-4, Claude, and Gemini are all foundation models. They're called 'foundation' models because they serve as the base that other, more specialized AI applications are built on top of.
Gemini
Google's flagship AI assistant and language model, available at gemini.google.com. Gemini integrates natively with Google Workspace — Gmail, Docs, Sheets, Drive — making it a strong choice for businesses already running on Google tools. It competes directly with ChatGPT and Claude and is available with a free tier and paid Google One AI Premium plan.
Grok
An AI assistant built by xAI, Elon Musk's AI company, and integrated into the X (formerly Twitter) platform. Grok is notable for having real-time access to information posted on X and for taking a less filtered approach to responses compared to some competitors. Available to X Premium subscribers and via the Grok standalone app.
Generative AI
AI that can create new content — text, images, audio, video, code — rather than just analysing or classifying existing content. ChatGPT generating a business proposal, DALL-E creating an image from a description, and GitHub Copilot writing code are all examples of generative AI in action.
GPT (Generative Pre-trained Transformer)
The type of AI model that powers ChatGPT, developed by OpenAI. GPT models are trained on vast amounts of text data and can generate human-like text, answer questions, summarize documents, write code, and much more. The 'transformer' part refers to the underlying architecture that makes modern AI so powerful.
Guardrails
Rules, filters, and safety mechanisms built into AI systems to prevent them from producing harmful, offensive, or inappropriate outputs. Guardrails are why AI assistants refuse certain requests. When building custom AI tools for businesses, we also add business-specific guardrails — for example, preventing a customer service bot from making promises the business can't keep.
Hallucination
When an AI confidently produces information that is factually incorrect or completely made up. This is one of the most important limitations of current AI tools to understand. AI doesn't 'know' things the way humans do — it predicts likely text, which can sometimes result in plausible-sounding but wrong answers. Always verify important facts.
Human-in-the-Loop
A system design where humans are involved in reviewing, approving, or correcting AI outputs before they're acted on. For most business applications, keeping a human in the loop is best practice — especially for anything customer-facing, financial, or legally sensitive.
Hyperparameter
Settings that control how an AI model is trained — like how fast it learns, how many training rounds it goes through, or how large its layers are. Unlike regular parameters (which the model learns automatically), hyperparameters are set by the people building the model. Getting them right is part of the craft of AI development.
Inference
The process of using a trained AI model to generate outputs from new inputs. When you type a question into ChatGPT and it responds, that's inference — the model is applying what it learned during training to your specific input. Training happens once; inference happens every time you use the tool.
Integration
Connecting two or more software systems so they can share data and work together. In AI, integration typically means connecting an AI tool to your existing business software — like linking a chatbot to your CRM, or connecting an automation tool to your booking system.
Jailbreak
An attempt to bypass the safety guidelines or restrictions built into an AI model — usually by crafting clever prompts that trick the AI into ignoring its rules. AI companies work continuously to prevent jailbreaks. As a business owner, it's worth knowing this exists so you understand why AI tools sometimes refuse certain requests — those guardrails are there for good reason.
Knowledge Base
A structured collection of information that an AI can draw on to answer questions. When we build a custom AI assistant for your business, we often create a knowledge base containing your FAQs, product details, policies, and procedures — so the AI gives accurate, business-specific answers rather than generic ones.
Large Language Model (LLM)
An AI model trained on enormous amounts of text data that can understand and generate human language. ChatGPT, Claude, and Gemini are all LLMs. They're called 'large' because of the sheer scale of data they're trained on and the number of parameters (internal settings) they contain — often in the billions.
Llama
Meta's family of open-source large language models, released for anyone to download, run, and build on. Unlike ChatGPT or Claude, Llama models can be run locally on your own hardware without sending data to a third-party server — making them popular for privacy-sensitive applications and businesses that want more control over their AI stack.
Machine Learning (ML)
A branch of AI where systems learn from data and improve their performance over time without being explicitly programmed for every scenario. Instead of following rigid rules, machine learning models identify patterns in data and use those patterns to make predictions or decisions.
Multimodal AI
AI that can process and generate multiple types of content — text, images, audio, and video — rather than just one. GPT-4o is multimodal: you can show it a photo and ask a question about it, or have it describe an image. Multimodal AI opens up a much wider range of business applications than text-only models.
Model
In AI, a model is the trained system that takes inputs and produces outputs. When people refer to 'the model', they mean the AI itself — the mathematical structure that has been trained on data. Different models have different strengths, weaknesses, and use cases.
Natural Language Processing (NLP)
The branch of AI focused on enabling computers to understand, interpret, and generate human language. NLP powers chatbots, voice assistants, translation tools, sentiment analysis, and much more. When an AI understands what you're asking in plain English, that's NLP at work.
Neural Network
A type of AI system loosely inspired by the structure of the human brain, made up of layers of interconnected nodes that process information. Neural networks are the foundation of most modern AI — including image recognition, speech recognition, and large language models.
No-Code / Low-Code
Tools and platforms that allow people to build software applications and automations without writing traditional code. Many AI tools are now available in no-code or low-code formats, making them accessible to business owners without technical backgrounds. Zapier and Make are popular examples.
OpenAI
The company behind ChatGPT, GPT-4, and DALL-E. OpenAI is one of the most influential AI research organizations in the world. Founded in 2015, it has become the most recognized name in consumer AI and provides the underlying technology for many third-party AI tools and applications.
Overfitting
When an AI model learns its training data too well — including the noise and quirks — and as a result performs poorly on new, real-world data. Think of it like a student who memorises past exam papers word-for-word but can't answer a question that's phrased slightly differently. Overfitting is a common challenge in building reliable AI systems.
Parameters
The internal settings of an AI model that are adjusted during training to improve performance. A model with more parameters is generally more capable but also more expensive to run. GPT-4 is estimated to have hundreds of billions of parameters — which is part of why it's so capable.
Prompt
The input you give to an AI model — the question, instruction, or context that tells the AI what you want it to do. Writing effective prompts (prompt engineering) is a skill that significantly affects the quality of AI outputs. A well-crafted prompt can be the difference between a mediocre and an excellent result.
Prompt Engineering
The practice of designing and refining prompts to get better outputs from AI models. It involves understanding how AI models interpret instructions and structuring your inputs to guide the model toward the response you want. Good prompt engineering is one of the most practical AI skills a business owner can develop.
Prompt Injection
A type of attack where a malicious user crafts inputs designed to override an AI system's instructions and make it behave in unintended ways. For example, a user might try to trick a customer service chatbot into ignoring its rules by embedding hidden instructions in their message. It's a real security concern for businesses deploying AI-facing tools — and something we account for when building AI systems for clients.
Query
A question or request submitted to an AI system, search engine, or database. In everyday AI use, a query is essentially what you type into ChatGPT or a search bar. In more technical contexts, queries are used to retrieve specific data from databases. The quality of your query directly affects the quality of the answer you get back.
RAG (Retrieval-Augmented Generation)
A technique that combines an AI language model with a search system, allowing the AI to retrieve relevant information from a specific knowledge base before generating a response. RAG is how you build AI tools that can answer questions about your specific business — your products, policies, FAQs — rather than just general knowledge.
Sentiment Analysis
An AI technique that identifies and categorises the emotional tone of text — positive, negative, or neutral. Businesses use sentiment analysis to monitor customer reviews, social media mentions, and support tickets to understand how customers feel about their products or services at scale.
Shadow AI
The use of AI tools by employees without the knowledge or approval of their organization's IT or management team. Just as 'shadow IT' described unauthorised software use, shadow AI is a growing concern — staff may be pasting sensitive client data into ChatGPT or using unapproved AI tools, creating data privacy and security risks. Having a clear AI policy in your business is increasingly important.
System Prompt
A set of instructions given to an AI model before a conversation begins, used to define its persona, rules, and behavior. When we build a custom AI chatbot for a business, the system prompt is where we tell it who it is, what it can and can't do, and how it should respond to customers.
Token
The basic unit of text that AI language models process. A token is roughly equivalent to a word or part of a word — 'hamburger' might be split into two tokens, while 'the' is one. AI models have token limits for inputs and outputs, and usage is often billed per token. Understanding tokens helps you use AI tools more efficiently.
Training
The process of teaching an AI model by exposing it to large amounts of data and adjusting its parameters to improve performance. Training a large AI model requires enormous computing power and can take weeks or months. Most businesses use pre-trained models rather than training their own from scratch.
Transformer
The underlying architecture that powers most modern AI language models, including GPT, Claude, and Gemini. Introduced by Google in 2017, the transformer architecture revolutionised AI by enabling models to process and understand context across long sequences of text far more effectively than previous approaches.
Use Case
A specific, practical application of a technology to solve a real problem. In AI, a use case might be 'using AI to automatically respond to common customer enquiries' or 'using AI to summarize meeting notes'. Identifying the right use cases for your business is the first step in any successful AI implementation.
Vector Database
A type of database designed to store and search data in the format that AI models use (vectors/embeddings). Vector databases are a key component of RAG systems — they allow AI tools to quickly find the most relevant information from a large knowledge base when answering a question.
Vibe Coding
A term coined in 2025 describing the practice of building software by describing what you want in plain English to an AI — and letting the AI write the code for you. Instead of learning to code, you 'vibe' with the AI: you describe the idea, review what it builds, give feedback, and iterate. Tools like Cursor, GitHub Copilot, and platforms like the one this website was built on make vibe coding accessible to non-developers. It's one of the most significant shifts in how software gets made.
Voice AI
AI systems that can understand and generate spoken language — enabling voice-based interactions with software. Voice AI powers smart speakers (Alexa, Siri, Google Assistant), AI phone receptionists, and voice-enabled customer service tools. For small businesses, voice AI can handle inbound calls, answer common questions, and route enquiries — 24 hours a day.
Workflow
A sequence of steps or tasks that make up a business process. In the context of AI and automation, a workflow is the series of actions that get triggered and executed automatically — for example, a new lead fills in a form, which triggers an AI to qualify them, send a welcome email, and add them to your CRM.
xAI
The AI company founded by Elon Musk in 2023, responsible for building Grok. xAI's stated mission is to understand the true nature of the universe through AI research. It's one of the newer major players in the AI space and is notable for its integration with the X (formerly Twitter) platform.
Yield (AI Output Quality)
In AI contexts, yield refers to the quality and usefulness of the outputs an AI produces relative to the inputs given. High-yield prompting means getting consistently valuable, accurate, and relevant responses. Improving your yield is largely about better prompts, clearer context, and choosing the right model for the task.
Zero-Shot Learning
The ability of an AI model to perform a task it hasn't been specifically trained on, using only a description or instruction. For example, asking ChatGPT to classify customer feedback into categories it's never seen before. Zero-shot capability is one of the things that makes modern LLMs so versatile for business use.
AI Washing
When a company exaggerates or falsely claims that its product uses AI to appear more innovative or attract investment. Similar to 'greenwashing' in sustainability. As AI becomes a buzzword, AI washing is increasingly common — it's worth asking vendors exactly how AI is used in their product, not just whether it is.
Algorithm
A set of step-by-step instructions a computer follows to complete a task or solve a problem. Think of it like a recipe — the computer follows each step in order to produce a result. Most AI tools are built on complex algorithms that process data and generate outputs.
Artificial Intelligence (AI)
Technology that enables computers to perform tasks that normally require human intelligence — things like understanding language, recognizing images, making decisions, and learning from experience. AI is an umbrella term that covers many different technologies and approaches.
Automation
Using technology to perform tasks with little or no human input. In a business context, automation means setting up systems that handle repetitive work automatically — like sending follow-up emails, updating spreadsheets, or routing customer enquiries — so your team can focus on higher-value work.
Agentic AI
AI that can take actions and make decisions autonomously to complete a goal — not just answer questions, but actually do things like browse the web, send emails, or interact with software on your behalf. Agentic AI is one of the fastest-growing areas of AI development.
AI-Native
A product, business, or workflow that is built from the ground up with AI at its core — rather than having AI bolted on as an afterthought. An AI-native business uses AI as a fundamental part of how it operates, not just as an occasional tool. Increasingly, being AI-native is a competitive advantage.
API (Application Programming Interface)
A way for two software applications to talk to each other. When we integrate an AI tool with your existing software, we're usually using APIs to connect them. You don't need to understand how APIs work — just know they're the plumbing that makes integrations possible.
ChatGPT
The AI assistant built by OpenAI and one of the most widely used AI tools in the world. ChatGPT can write, summarize, answer questions, draft emails, generate ideas, and much more — all through a simple chat interface. It runs on OpenAI's GPT models and is available free or via a paid plan. For most small businesses, it's the best starting point for exploring what AI can do.
Claude
An AI assistant built by Anthropic, widely regarded as one of the best alternatives to ChatGPT. Claude is known for nuanced writing, long-document analysis, and following complex instructions carefully. It's designed with a strong focus on safety and honesty. Available at claude.ai with a free tier and paid plans.
Bot
Short for 'robot' — a software program that performs automated tasks. In business, bots are commonly used for customer service (chatbots), data collection, and repetitive admin tasks. Not all bots use AI, but AI-powered bots are far more capable than simple rule-based ones.
Benchmark
A standardised test used to measure and compare the performance of AI models. When you see claims like 'our AI scores 90% on X benchmark', it means the model was tested against a standard set of problems. Benchmarks help compare different AI tools, though real-world performance can vary.
Black Box
A system where you can see the inputs and outputs but not what happens in between. Most AI models are black boxes — you give them a prompt and get a response, but the internal reasoning is not visible or easily explainable. This is why 'explainable AI' is an active area of research, particularly in regulated industries like finance and healthcare.
Chatbot
A software program designed to simulate conversation with humans, typically via text. Modern AI-powered chatbots (like those built on ChatGPT) can understand natural language, answer complex questions, and handle customer service tasks around the clock — far beyond the simple 'press 1 for billing' bots of the past.
Context Window
The amount of text an AI model can 'see' and consider at one time. Think of it like short-term memory — the larger the context window, the more of a conversation or document the AI can take into account when generating a response. Larger context windows generally produce more coherent, relevant outputs.
Copilot
An AI assistant that works alongside a human to help them do their job better — not replacing them, but augmenting their capabilities. Microsoft's Copilot (built into Office 365) is a well-known example. The term reflects the idea that the human is still in control; the AI is just helping navigate.
Data
Information in a form that computers can process. AI systems learn from data — the more relevant, high-quality data an AI is trained on, the better it tends to perform. In a business context, your data includes things like customer records, transaction history, emails, and documents.
Deepfake
AI-generated video, audio, or images that realistically depict someone saying or doing something they never actually said or did. Deepfakes are created using deep learning techniques and have become increasingly convincing. As a business owner, it's important to be aware of deepfakes — both as a potential security risk (e.g. fake audio of a CEO authorising a transfer) and as a reputational concern.
Deep Learning
A type of machine learning that uses neural networks with many layers to learn from large amounts of data. Deep learning powers many of the most impressive AI capabilities today — including image recognition, speech recognition, and large language models like ChatGPT.
Embedding
A way of representing text, images, or other data as numbers so that an AI can understand relationships between concepts. Embeddings allow AI to understand that 'dog' and 'puppy' are related, or that 'invoice' and 'bill' mean similar things — even without being explicitly told.
Emergent Behavior
Unexpected capabilities that appear in AI models as they grow larger or are trained on more data — abilities that weren't explicitly programmed or anticipated. For example, large language models spontaneously developed the ability to do basic arithmetic and translate languages without being specifically trained to do so. Emergent behavior is one of the reasons AI development can be unpredictable.
Fine-Tuning
The process of taking a pre-trained AI model and training it further on a specific dataset to make it better at a particular task. For example, fine-tuning a general language model on your company's documents and tone of voice so it writes in your style and understands your business context.
Frontier Model
The most advanced AI models available at any given time — the cutting edge of what's technically possible. GPT-4o, Claude 3.5 Sonnet, and Gemini Ultra are examples of frontier models. The term is used in AI policy and safety discussions to describe models powerful enough to potentially pose novel risks alongside their capabilities.
Foundation Model
A large AI model trained on a broad dataset that can be adapted for many different tasks. GPT-4, Claude, and Gemini are all foundation models. They're called 'foundation' models because they serve as the base that other, more specialized AI applications are built on top of.
Gemini
Google's flagship AI assistant and language model, available at gemini.google.com. Gemini integrates natively with Google Workspace — Gmail, Docs, Sheets, Drive — making it a strong choice for businesses already running on Google tools. It competes directly with ChatGPT and Claude and is available with a free tier and paid Google One AI Premium plan.
Grok
An AI assistant built by xAI, Elon Musk's AI company, and integrated into the X (formerly Twitter) platform. Grok is notable for having real-time access to information posted on X and for taking a less filtered approach to responses compared to some competitors. Available to X Premium subscribers and via the Grok standalone app.
Generative AI
AI that can create new content — text, images, audio, video, code — rather than just analysing or classifying existing content. ChatGPT generating a business proposal, DALL-E creating an image from a description, and GitHub Copilot writing code are all examples of generative AI in action.
GPT (Generative Pre-trained Transformer)
The type of AI model that powers ChatGPT, developed by OpenAI. GPT models are trained on vast amounts of text data and can generate human-like text, answer questions, summarize documents, write code, and much more. The 'transformer' part refers to the underlying architecture that makes modern AI so powerful.
Guardrails
Rules, filters, and safety mechanisms built into AI systems to prevent them from producing harmful, offensive, or inappropriate outputs. Guardrails are why AI assistants refuse certain requests. When building custom AI tools for businesses, we also add business-specific guardrails — for example, preventing a customer service bot from making promises the business can't keep.
Hallucination
When an AI confidently produces information that is factually incorrect or completely made up. This is one of the most important limitations of current AI tools to understand. AI doesn't 'know' things the way humans do — it predicts likely text, which can sometimes result in plausible-sounding but wrong answers. Always verify important facts.
Human-in-the-Loop
A system design where humans are involved in reviewing, approving, or correcting AI outputs before they're acted on. For most business applications, keeping a human in the loop is best practice — especially for anything customer-facing, financial, or legally sensitive.
Hyperparameter
Settings that control how an AI model is trained — like how fast it learns, how many training rounds it goes through, or how large its layers are. Unlike regular parameters (which the model learns automatically), hyperparameters are set by the people building the model. Getting them right is part of the craft of AI development.
Inference
The process of using a trained AI model to generate outputs from new inputs. When you type a question into ChatGPT and it responds, that's inference — the model is applying what it learned during training to your specific input. Training happens once; inference happens every time you use the tool.
Integration
Connecting two or more software systems so they can share data and work together. In AI, integration typically means connecting an AI tool to your existing business software — like linking a chatbot to your CRM, or connecting an automation tool to your booking system.
Jailbreak
An attempt to bypass the safety guidelines or restrictions built into an AI model — usually by crafting clever prompts that trick the AI into ignoring its rules. AI companies work continuously to prevent jailbreaks. As a business owner, it's worth knowing this exists so you understand why AI tools sometimes refuse certain requests — those guardrails are there for good reason.
Knowledge Base
A structured collection of information that an AI can draw on to answer questions. When we build a custom AI assistant for your business, we often create a knowledge base containing your FAQs, product details, policies, and procedures — so the AI gives accurate, business-specific answers rather than generic ones.
Large Language Model (LLM)
An AI model trained on enormous amounts of text data that can understand and generate human language. ChatGPT, Claude, and Gemini are all LLMs. They're called 'large' because of the sheer scale of data they're trained on and the number of parameters (internal settings) they contain — often in the billions.
Llama
Meta's family of open-source large language models, released for anyone to download, run, and build on. Unlike ChatGPT or Claude, Llama models can be run locally on your own hardware without sending data to a third-party server — making them popular for privacy-sensitive applications and businesses that want more control over their AI stack.
Machine Learning (ML)
A branch of AI where systems learn from data and improve their performance over time without being explicitly programmed for every scenario. Instead of following rigid rules, machine learning models identify patterns in data and use those patterns to make predictions or decisions.
Multimodal AI
AI that can process and generate multiple types of content — text, images, audio, and video — rather than just one. GPT-4o is multimodal: you can show it a photo and ask a question about it, or have it describe an image. Multimodal AI opens up a much wider range of business applications than text-only models.
Model
In AI, a model is the trained system that takes inputs and produces outputs. When people refer to 'the model', they mean the AI itself — the mathematical structure that has been trained on data. Different models have different strengths, weaknesses, and use cases.
Natural Language Processing (NLP)
The branch of AI focused on enabling computers to understand, interpret, and generate human language. NLP powers chatbots, voice assistants, translation tools, sentiment analysis, and much more. When an AI understands what you're asking in plain English, that's NLP at work.
Neural Network
A type of AI system loosely inspired by the structure of the human brain, made up of layers of interconnected nodes that process information. Neural networks are the foundation of most modern AI — including image recognition, speech recognition, and large language models.
No-Code / Low-Code
Tools and platforms that allow people to build software applications and automations without writing traditional code. Many AI tools are now available in no-code or low-code formats, making them accessible to business owners without technical backgrounds. Zapier and Make are popular examples.
OpenAI
The company behind ChatGPT, GPT-4, and DALL-E. OpenAI is one of the most influential AI research organizations in the world. Founded in 2015, it has become the most recognized name in consumer AI and provides the underlying technology for many third-party AI tools and applications.
Overfitting
When an AI model learns its training data too well — including the noise and quirks — and as a result performs poorly on new, real-world data. Think of it like a student who memorises past exam papers word-for-word but can't answer a question that's phrased slightly differently. Overfitting is a common challenge in building reliable AI systems.
Parameters
The internal settings of an AI model that are adjusted during training to improve performance. A model with more parameters is generally more capable but also more expensive to run. GPT-4 is estimated to have hundreds of billions of parameters — which is part of why it's so capable.
Prompt
The input you give to an AI model — the question, instruction, or context that tells the AI what you want it to do. Writing effective prompts (prompt engineering) is a skill that significantly affects the quality of AI outputs. A well-crafted prompt can be the difference between a mediocre and an excellent result.
Prompt Engineering
The practice of designing and refining prompts to get better outputs from AI models. It involves understanding how AI models interpret instructions and structuring your inputs to guide the model toward the response you want. Good prompt engineering is one of the most practical AI skills a business owner can develop.
Prompt Injection
A type of attack where a malicious user crafts inputs designed to override an AI system's instructions and make it behave in unintended ways. For example, a user might try to trick a customer service chatbot into ignoring its rules by embedding hidden instructions in their message. It's a real security concern for businesses deploying AI-facing tools — and something we account for when building AI systems for clients.
Query
A question or request submitted to an AI system, search engine, or database. In everyday AI use, a query is essentially what you type into ChatGPT or a search bar. In more technical contexts, queries are used to retrieve specific data from databases. The quality of your query directly affects the quality of the answer you get back.
RAG (Retrieval-Augmented Generation)
A technique that combines an AI language model with a search system, allowing the AI to retrieve relevant information from a specific knowledge base before generating a response. RAG is how you build AI tools that can answer questions about your specific business — your products, policies, FAQs — rather than just general knowledge.
Sentiment Analysis
An AI technique that identifies and categorises the emotional tone of text — positive, negative, or neutral. Businesses use sentiment analysis to monitor customer reviews, social media mentions, and support tickets to understand how customers feel about their products or services at scale.
Shadow AI
The use of AI tools by employees without the knowledge or approval of their organization's IT or management team. Just as 'shadow IT' described unauthorised software use, shadow AI is a growing concern — staff may be pasting sensitive client data into ChatGPT or using unapproved AI tools, creating data privacy and security risks. Having a clear AI policy in your business is increasingly important.
System Prompt
A set of instructions given to an AI model before a conversation begins, used to define its persona, rules, and behavior. When we build a custom AI chatbot for a business, the system prompt is where we tell it who it is, what it can and can't do, and how it should respond to customers.
Token
The basic unit of text that AI language models process. A token is roughly equivalent to a word or part of a word — 'hamburger' might be split into two tokens, while 'the' is one. AI models have token limits for inputs and outputs, and usage is often billed per token. Understanding tokens helps you use AI tools more efficiently.
Training
The process of teaching an AI model by exposing it to large amounts of data and adjusting its parameters to improve performance. Training a large AI model requires enormous computing power and can take weeks or months. Most businesses use pre-trained models rather than training their own from scratch.
Transformer
The underlying architecture that powers most modern AI language models, including GPT, Claude, and Gemini. Introduced by Google in 2017, the transformer architecture revolutionised AI by enabling models to process and understand context across long sequences of text far more effectively than previous approaches.
Use Case
A specific, practical application of a technology to solve a real problem. In AI, a use case might be 'using AI to automatically respond to common customer enquiries' or 'using AI to summarize meeting notes'. Identifying the right use cases for your business is the first step in any successful AI implementation.
Vector Database
A type of database designed to store and search data in the format that AI models use (vectors/embeddings). Vector databases are a key component of RAG systems — they allow AI tools to quickly find the most relevant information from a large knowledge base when answering a question.
Vibe Coding
A term coined in 2025 describing the practice of building software by describing what you want in plain English to an AI — and letting the AI write the code for you. Instead of learning to code, you 'vibe' with the AI: you describe the idea, review what it builds, give feedback, and iterate. Tools like Cursor, GitHub Copilot, and platforms like the one this website was built on make vibe coding accessible to non-developers. It's one of the most significant shifts in how software gets made.
Voice AI
AI systems that can understand and generate spoken language — enabling voice-based interactions with software. Voice AI powers smart speakers (Alexa, Siri, Google Assistant), AI phone receptionists, and voice-enabled customer service tools. For small businesses, voice AI can handle inbound calls, answer common questions, and route enquiries — 24 hours a day.
Workflow
A sequence of steps or tasks that make up a business process. In the context of AI and automation, a workflow is the series of actions that get triggered and executed automatically — for example, a new lead fills in a form, which triggers an AI to qualify them, send a welcome email, and add them to your CRM.
xAI
The AI company founded by Elon Musk in 2023, responsible for building Grok. xAI's stated mission is to understand the true nature of the universe through AI research. It's one of the newer major players in the AI space and is notable for its integration with the X (formerly Twitter) platform.
Yield (AI Output Quality)
In AI contexts, yield refers to the quality and usefulness of the outputs an AI produces relative to the inputs given. High-yield prompting means getting consistently valuable, accurate, and relevant responses. Improving your yield is largely about better prompts, clearer context, and choosing the right model for the task.
Zero-Shot Learning
The ability of an AI model to perform a task it hasn't been specifically trained on, using only a description or instruction. For example, asking ChatGPT to classify customer feedback into categories it's never seen before. Zero-shot capability is one of the things that makes modern LLMs so versatile for business use.
AI Washing
When a company exaggerates or falsely claims that its product uses AI to appear more innovative or attract investment. Similar to 'greenwashing' in sustainability. As AI becomes a buzzword, AI washing is increasingly common — it's worth asking vendors exactly how AI is used in their product, not just whether it is.
Algorithm
A set of step-by-step instructions a computer follows to complete a task or solve a problem. Think of it like a recipe — the computer follows each step in order to produce a result. Most AI tools are built on complex algorithms that process data and generate outputs.
Artificial Intelligence (AI)
Technology that enables computers to perform tasks that normally require human intelligence — things like understanding language, recognizing images, making decisions, and learning from experience. AI is an umbrella term that covers many different technologies and approaches.
Automation
Using technology to perform tasks with little or no human input. In a business context, automation means setting up systems that handle repetitive work automatically — like sending follow-up emails, updating spreadsheets, or routing customer enquiries — so your team can focus on higher-value work.
Agentic AI
AI that can take actions and make decisions autonomously to complete a goal — not just answer questions, but actually do things like browse the web, send emails, or interact with software on your behalf. Agentic AI is one of the fastest-growing areas of AI development.
AI-Native
A product, business, or workflow that is built from the ground up with AI at its core — rather than having AI bolted on as an afterthought. An AI-native business uses AI as a fundamental part of how it operates, not just as an occasional tool. Increasingly, being AI-native is a competitive advantage.
API (Application Programming Interface)
A way for two software applications to talk to each other. When we integrate an AI tool with your existing software, we're usually using APIs to connect them. You don't need to understand how APIs work — just know they're the plumbing that makes integrations possible.
ChatGPT
The AI assistant built by OpenAI and one of the most widely used AI tools in the world. ChatGPT can write, summarize, answer questions, draft emails, generate ideas, and much more — all through a simple chat interface. It runs on OpenAI's GPT models and is available free or via a paid plan. For most small businesses, it's the best starting point for exploring what AI can do.
Claude
An AI assistant built by Anthropic, widely regarded as one of the best alternatives to ChatGPT. Claude is known for nuanced writing, long-document analysis, and following complex instructions carefully. It's designed with a strong focus on safety and honesty. Available at claude.ai with a free tier and paid plans.
Bot
Short for 'robot' — a software program that performs automated tasks. In business, bots are commonly used for customer service (chatbots), data collection, and repetitive admin tasks. Not all bots use AI, but AI-powered bots are far more capable than simple rule-based ones.
Benchmark
A standardised test used to measure and compare the performance of AI models. When you see claims like 'our AI scores 90% on X benchmark', it means the model was tested against a standard set of problems. Benchmarks help compare different AI tools, though real-world performance can vary.
Black Box
A system where you can see the inputs and outputs but not what happens in between. Most AI models are black boxes — you give them a prompt and get a response, but the internal reasoning is not visible or easily explainable. This is why 'explainable AI' is an active area of research, particularly in regulated industries like finance and healthcare.
Chatbot
A software program designed to simulate conversation with humans, typically via text. Modern AI-powered chatbots (like those built on ChatGPT) can understand natural language, answer complex questions, and handle customer service tasks around the clock — far beyond the simple 'press 1 for billing' bots of the past.
Context Window
The amount of text an AI model can 'see' and consider at one time. Think of it like short-term memory — the larger the context window, the more of a conversation or document the AI can take into account when generating a response. Larger context windows generally produce more coherent, relevant outputs.
Copilot
An AI assistant that works alongside a human to help them do their job better — not replacing them, but augmenting their capabilities. Microsoft's Copilot (built into Office 365) is a well-known example. The term reflects the idea that the human is still in control; the AI is just helping navigate.
Data
Information in a form that computers can process. AI systems learn from data — the more relevant, high-quality data an AI is trained on, the better it tends to perform. In a business context, your data includes things like customer records, transaction history, emails, and documents.
Deepfake
AI-generated video, audio, or images that realistically depict someone saying or doing something they never actually said or did. Deepfakes are created using deep learning techniques and have become increasingly convincing. As a business owner, it's important to be aware of deepfakes — both as a potential security risk (e.g. fake audio of a CEO authorising a transfer) and as a reputational concern.
Deep Learning
A type of machine learning that uses neural networks with many layers to learn from large amounts of data. Deep learning powers many of the most impressive AI capabilities today — including image recognition, speech recognition, and large language models like ChatGPT.
Embedding
A way of representing text, images, or other data as numbers so that an AI can understand relationships between concepts. Embeddings allow AI to understand that 'dog' and 'puppy' are related, or that 'invoice' and 'bill' mean similar things — even without being explicitly told.
Emergent Behavior
Unexpected capabilities that appear in AI models as they grow larger or are trained on more data — abilities that weren't explicitly programmed or anticipated. For example, large language models spontaneously developed the ability to do basic arithmetic and translate languages without being specifically trained to do so. Emergent behavior is one of the reasons AI development can be unpredictable.
Fine-Tuning
The process of taking a pre-trained AI model and training it further on a specific dataset to make it better at a particular task. For example, fine-tuning a general language model on your company's documents and tone of voice so it writes in your style and understands your business context.
Frontier Model
The most advanced AI models available at any given time — the cutting edge of what's technically possible. GPT-4o, Claude 3.5 Sonnet, and Gemini Ultra are examples of frontier models. The term is used in AI policy and safety discussions to describe models powerful enough to potentially pose novel risks alongside their capabilities.
Foundation Model
A large AI model trained on a broad dataset that can be adapted for many different tasks. GPT-4, Claude, and Gemini are all foundation models. They're called 'foundation' models because they serve as the base that other, more specialized AI applications are built on top of.
Gemini
Google's flagship AI assistant and language model, available at gemini.google.com. Gemini integrates natively with Google Workspace — Gmail, Docs, Sheets, Drive — making it a strong choice for businesses already running on Google tools. It competes directly with ChatGPT and Claude and is available with a free tier and paid Google One AI Premium plan.
Grok
An AI assistant built by xAI, Elon Musk's AI company, and integrated into the X (formerly Twitter) platform. Grok is notable for having real-time access to information posted on X and for taking a less filtered approach to responses compared to some competitors. Available to X Premium subscribers and via the Grok standalone app.
Generative AI
AI that can create new content — text, images, audio, video, code — rather than just analysing or classifying existing content. ChatGPT generating a business proposal, DALL-E creating an image from a description, and GitHub Copilot writing code are all examples of generative AI in action.
GPT (Generative Pre-trained Transformer)
The type of AI model that powers ChatGPT, developed by OpenAI. GPT models are trained on vast amounts of text data and can generate human-like text, answer questions, summarize documents, write code, and much more. The 'transformer' part refers to the underlying architecture that makes modern AI so powerful.
Guardrails
Rules, filters, and safety mechanisms built into AI systems to prevent them from producing harmful, offensive, or inappropriate outputs. Guardrails are why AI assistants refuse certain requests. When building custom AI tools for businesses, we also add business-specific guardrails — for example, preventing a customer service bot from making promises the business can't keep.
Hallucination
When an AI confidently produces information that is factually incorrect or completely made up. This is one of the most important limitations of current AI tools to understand. AI doesn't 'know' things the way humans do — it predicts likely text, which can sometimes result in plausible-sounding but wrong answers. Always verify important facts.
Human-in-the-Loop
A system design where humans are involved in reviewing, approving, or correcting AI outputs before they're acted on. For most business applications, keeping a human in the loop is best practice — especially for anything customer-facing, financial, or legally sensitive.
Hyperparameter
Settings that control how an AI model is trained — like how fast it learns, how many training rounds it goes through, or how large its layers are. Unlike regular parameters (which the model learns automatically), hyperparameters are set by the people building the model. Getting them right is part of the craft of AI development.
Inference
The process of using a trained AI model to generate outputs from new inputs. When you type a question into ChatGPT and it responds, that's inference — the model is applying what it learned during training to your specific input. Training happens once; inference happens every time you use the tool.
Integration
Connecting two or more software systems so they can share data and work together. In AI, integration typically means connecting an AI tool to your existing business software — like linking a chatbot to your CRM, or connecting an automation tool to your booking system.
Jailbreak
An attempt to bypass the safety guidelines or restrictions built into an AI model — usually by crafting clever prompts that trick the AI into ignoring its rules. AI companies work continuously to prevent jailbreaks. As a business owner, it's worth knowing this exists so you understand why AI tools sometimes refuse certain requests — those guardrails are there for good reason.
Knowledge Base
A structured collection of information that an AI can draw on to answer questions. When we build a custom AI assistant for your business, we often create a knowledge base containing your FAQs, product details, policies, and procedures — so the AI gives accurate, business-specific answers rather than generic ones.
Large Language Model (LLM)
An AI model trained on enormous amounts of text data that can understand and generate human language. ChatGPT, Claude, and Gemini are all LLMs. They're called 'large' because of the sheer scale of data they're trained on and the number of parameters (internal settings) they contain — often in the billions.
Llama
Meta's family of open-source large language models, released for anyone to download, run, and build on. Unlike ChatGPT or Claude, Llama models can be run locally on your own hardware without sending data to a third-party server — making them popular for privacy-sensitive applications and businesses that want more control over their AI stack.
Machine Learning (ML)
A branch of AI where systems learn from data and improve their performance over time without being explicitly programmed for every scenario. Instead of following rigid rules, machine learning models identify patterns in data and use those patterns to make predictions or decisions.
Multimodal AI
AI that can process and generate multiple types of content — text, images, audio, and video — rather than just one. GPT-4o is multimodal: you can show it a photo and ask a question about it, or have it describe an image. Multimodal AI opens up a much wider range of business applications than text-only models.
Model
In AI, a model is the trained system that takes inputs and produces outputs. When people refer to 'the model', they mean the AI itself — the mathematical structure that has been trained on data. Different models have different strengths, weaknesses, and use cases.
Natural Language Processing (NLP)
The branch of AI focused on enabling computers to understand, interpret, and generate human language. NLP powers chatbots, voice assistants, translation tools, sentiment analysis, and much more. When an AI understands what you're asking in plain English, that's NLP at work.
Neural Network
A type of AI system loosely inspired by the structure of the human brain, made up of layers of interconnected nodes that process information. Neural networks are the foundation of most modern AI — including image recognition, speech recognition, and large language models.
No-Code / Low-Code
Tools and platforms that allow people to build software applications and automations without writing traditional code. Many AI tools are now available in no-code or low-code formats, making them accessible to business owners without technical backgrounds. Zapier and Make are popular examples.
OpenAI
The company behind ChatGPT, GPT-4, and DALL-E. OpenAI is one of the most influential AI research organizations in the world. Founded in 2015, it has become the most recognized name in consumer AI and provides the underlying technology for many third-party AI tools and applications.
Overfitting
When an AI model learns its training data too well — including the noise and quirks — and as a result performs poorly on new, real-world data. Think of it like a student who memorises past exam papers word-for-word but can't answer a question that's phrased slightly differently. Overfitting is a common challenge in building reliable AI systems.
Parameters
The internal settings of an AI model that are adjusted during training to improve performance. A model with more parameters is generally more capable but also more expensive to run. GPT-4 is estimated to have hundreds of billions of parameters — which is part of why it's so capable.
Prompt
The input you give to an AI model — the question, instruction, or context that tells the AI what you want it to do. Writing effective prompts (prompt engineering) is a skill that significantly affects the quality of AI outputs. A well-crafted prompt can be the difference between a mediocre and an excellent result.
Prompt Engineering
The practice of designing and refining prompts to get better outputs from AI models. It involves understanding how AI models interpret instructions and structuring your inputs to guide the model toward the response you want. Good prompt engineering is one of the most practical AI skills a business owner can develop.
Prompt Injection
A type of attack where a malicious user crafts inputs designed to override an AI system's instructions and make it behave in unintended ways. For example, a user might try to trick a customer service chatbot into ignoring its rules by embedding hidden instructions in their message. It's a real security concern for businesses deploying AI-facing tools — and something we account for when building AI systems for clients.
Query
A question or request submitted to an AI system, search engine, or database. In everyday AI use, a query is essentially what you type into ChatGPT or a search bar. In more technical contexts, queries are used to retrieve specific data from databases. The quality of your query directly affects the quality of the answer you get back.
RAG (Retrieval-Augmented Generation)
A technique that combines an AI language model with a search system, allowing the AI to retrieve relevant information from a specific knowledge base before generating a response. RAG is how you build AI tools that can answer questions about your specific business — your products, policies, FAQs — rather than just general knowledge.
Sentiment Analysis
An AI technique that identifies and categorises the emotional tone of text — positive, negative, or neutral. Businesses use sentiment analysis to monitor customer reviews, social media mentions, and support tickets to understand how customers feel about their products or services at scale.
Shadow AI
The use of AI tools by employees without the knowledge or approval of their organization's IT or management team. Just as 'shadow IT' described unauthorised software use, shadow AI is a growing concern — staff may be pasting sensitive client data into ChatGPT or using unapproved AI tools, creating data privacy and security risks. Having a clear AI policy in your business is increasingly important.
System Prompt
A set of instructions given to an AI model before a conversation begins, used to define its persona, rules, and behavior. When we build a custom AI chatbot for a business, the system prompt is where we tell it who it is, what it can and can't do, and how it should respond to customers.
Token
The basic unit of text that AI language models process. A token is roughly equivalent to a word or part of a word — 'hamburger' might be split into two tokens, while 'the' is one. AI models have token limits for inputs and outputs, and usage is often billed per token. Understanding tokens helps you use AI tools more efficiently.
Training
The process of teaching an AI model by exposing it to large amounts of data and adjusting its parameters to improve performance. Training a large AI model requires enormous computing power and can take weeks or months. Most businesses use pre-trained models rather than training their own from scratch.
Transformer
The underlying architecture that powers most modern AI language models, including GPT, Claude, and Gemini. Introduced by Google in 2017, the transformer architecture revolutionised AI by enabling models to process and understand context across long sequences of text far more effectively than previous approaches.
Use Case
A specific, practical application of a technology to solve a real problem. In AI, a use case might be 'using AI to automatically respond to common customer enquiries' or 'using AI to summarize meeting notes'. Identifying the right use cases for your business is the first step in any successful AI implementation.
Vector Database
A type of database designed to store and search data in the format that AI models use (vectors/embeddings). Vector databases are a key component of RAG systems — they allow AI tools to quickly find the most relevant information from a large knowledge base when answering a question.
Vibe Coding
A term coined in 2025 describing the practice of building software by describing what you want in plain English to an AI — and letting the AI write the code for you. Instead of learning to code, you 'vibe' with the AI: you describe the idea, review what it builds, give feedback, and iterate. Tools like Cursor, GitHub Copilot, and platforms like the one this website was built on make vibe coding accessible to non-developers. It's one of the most significant shifts in how software gets made.
Voice AI
AI systems that can understand and generate spoken language — enabling voice-based interactions with software. Voice AI powers smart speakers (Alexa, Siri, Google Assistant), AI phone receptionists, and voice-enabled customer service tools. For small businesses, voice AI can handle inbound calls, answer common questions, and route enquiries — 24 hours a day.
Workflow
A sequence of steps or tasks that make up a business process. In the context of AI and automation, a workflow is the series of actions that get triggered and executed automatically — for example, a new lead fills in a form, which triggers an AI to qualify them, send a welcome email, and add them to your CRM.
xAI
The AI company founded by Elon Musk in 2023, responsible for building Grok. xAI's stated mission is to understand the true nature of the universe through AI research. It's one of the newer major players in the AI space and is notable for its integration with the X (formerly Twitter) platform.
Yield (AI Output Quality)
In AI contexts, yield refers to the quality and usefulness of the outputs an AI produces relative to the inputs given. High-yield prompting means getting consistently valuable, accurate, and relevant responses. Improving your yield is largely about better prompts, clearer context, and choosing the right model for the task.
Zero-Shot Learning
The ability of an AI model to perform a task it hasn't been specifically trained on, using only a description or instruction. For example, asking ChatGPT to classify customer feedback into categories it's never seen before. Zero-shot capability is one of the things that makes modern LLMs so versatile for business use.
AI Washing
When a company exaggerates or falsely claims that its product uses AI to appear more innovative or attract investment. Similar to 'greenwashing' in sustainability. As AI becomes a buzzword, AI washing is increasingly common — it's worth asking vendors exactly how AI is used in their product, not just whether it is.
Algorithm
A set of step-by-step instructions a computer follows to complete a task or solve a problem. Think of it like a recipe — the computer follows each step in order to produce a result. Most AI tools are built on complex algorithms that process data and generate outputs.
Artificial Intelligence (AI)
Technology that enables computers to perform tasks that normally require human intelligence — things like understanding language, recognizing images, making decisions, and learning from experience. AI is an umbrella term that covers many different technologies and approaches.
Automation
Using technology to perform tasks with little or no human input. In a business context, automation means setting up systems that handle repetitive work automatically — like sending follow-up emails, updating spreadsheets, or routing customer enquiries — so your team can focus on higher-value work.
Agentic AI
AI that can take actions and make decisions autonomously to complete a goal — not just answer questions, but actually do things like browse the web, send emails, or interact with software on your behalf. Agentic AI is one of the fastest-growing areas of AI development.
AI-Native
A product, business, or workflow that is built from the ground up with AI at its core — rather than having AI bolted on as an afterthought. An AI-native business uses AI as a fundamental part of how it operates, not just as an occasional tool. Increasingly, being AI-native is a competitive advantage.
API (Application Programming Interface)
A way for two software applications to talk to each other. When we integrate an AI tool with your existing software, we're usually using APIs to connect them. You don't need to understand how APIs work — just know they're the plumbing that makes integrations possible.
ChatGPT
The AI assistant built by OpenAI and one of the most widely used AI tools in the world. ChatGPT can write, summarize, answer questions, draft emails, generate ideas, and much more — all through a simple chat interface. It runs on OpenAI's GPT models and is available free or via a paid plan. For most small businesses, it's the best starting point for exploring what AI can do.
Claude
An AI assistant built by Anthropic, widely regarded as one of the best alternatives to ChatGPT. Claude is known for nuanced writing, long-document analysis, and following complex instructions carefully. It's designed with a strong focus on safety and honesty. Available at claude.ai with a free tier and paid plans.
Bot
Short for 'robot' — a software program that performs automated tasks. In business, bots are commonly used for customer service (chatbots), data collection, and repetitive admin tasks. Not all bots use AI, but AI-powered bots are far more capable than simple rule-based ones.
Benchmark
A standardised test used to measure and compare the performance of AI models. When you see claims like 'our AI scores 90% on X benchmark', it means the model was tested against a standard set of problems. Benchmarks help compare different AI tools, though real-world performance can vary.
Black Box
A system where you can see the inputs and outputs but not what happens in between. Most AI models are black boxes — you give them a prompt and get a response, but the internal reasoning is not visible or easily explainable. This is why 'explainable AI' is an active area of research, particularly in regulated industries like finance and healthcare.
Chatbot
A software program designed to simulate conversation with humans, typically via text. Modern AI-powered chatbots (like those built on ChatGPT) can understand natural language, answer complex questions, and handle customer service tasks around the clock — far beyond the simple 'press 1 for billing' bots of the past.
Context Window
The amount of text an AI model can 'see' and consider at one time. Think of it like short-term memory — the larger the context window, the more of a conversation or document the AI can take into account when generating a response. Larger context windows generally produce more coherent, relevant outputs.
Copilot
An AI assistant that works alongside a human to help them do their job better — not replacing them, but augmenting their capabilities. Microsoft's Copilot (built into Office 365) is a well-known example. The term reflects the idea that the human is still in control; the AI is just helping navigate.
Data
Information in a form that computers can process. AI systems learn from data — the more relevant, high-quality data an AI is trained on, the better it tends to perform. In a business context, your data includes things like customer records, transaction history, emails, and documents.
Deepfake
AI-generated video, audio, or images that realistically depict someone saying or doing something they never actually said or did. Deepfakes are created using deep learning techniques and have become increasingly convincing. As a business owner, it's important to be aware of deepfakes — both as a potential security risk (e.g. fake audio of a CEO authorising a transfer) and as a reputational concern.
Deep Learning
A type of machine learning that uses neural networks with many layers to learn from large amounts of data. Deep learning powers many of the most impressive AI capabilities today — including image recognition, speech recognition, and large language models like ChatGPT.
Embedding
A way of representing text, images, or other data as numbers so that an AI can understand relationships between concepts. Embeddings allow AI to understand that 'dog' and 'puppy' are related, or that 'invoice' and 'bill' mean similar things — even without being explicitly told.
Emergent Behavior
Unexpected capabilities that appear in AI models as they grow larger or are trained on more data — abilities that weren't explicitly programmed or anticipated. For example, large language models spontaneously developed the ability to do basic arithmetic and translate languages without being specifically trained to do so. Emergent behavior is one of the reasons AI development can be unpredictable.
Fine-Tuning
The process of taking a pre-trained AI model and training it further on a specific dataset to make it better at a particular task. For example, fine-tuning a general language model on your company's documents and tone of voice so it writes in your style and understands your business context.
Frontier Model
The most advanced AI models available at any given time — the cutting edge of what's technically possible. GPT-4o, Claude 3.5 Sonnet, and Gemini Ultra are examples of frontier models. The term is used in AI policy and safety discussions to describe models powerful enough to potentially pose novel risks alongside their capabilities.
Foundation Model
A large AI model trained on a broad dataset that can be adapted for many different tasks. GPT-4, Claude, and Gemini are all foundation models. They're called 'foundation' models because they serve as the base that other, more specialized AI applications are built on top of.
Gemini
Google's flagship AI assistant and language model, available at gemini.google.com. Gemini integrates natively with Google Workspace — Gmail, Docs, Sheets, Drive — making it a strong choice for businesses already running on Google tools. It competes directly with ChatGPT and Claude and is available with a free tier and paid Google One AI Premium plan.
Grok
An AI assistant built by xAI, Elon Musk's AI company, and integrated into the X (formerly Twitter) platform. Grok is notable for having real-time access to information posted on X and for taking a less filtered approach to responses compared to some competitors. Available to X Premium subscribers and via the Grok standalone app.
Generative AI
AI that can create new content — text, images, audio, video, code — rather than just analysing or classifying existing content. ChatGPT generating a business proposal, DALL-E creating an image from a description, and GitHub Copilot writing code are all examples of generative AI in action.
GPT (Generative Pre-trained Transformer)
The type of AI model that powers ChatGPT, developed by OpenAI. GPT models are trained on vast amounts of text data and can generate human-like text, answer questions, summarize documents, write code, and much more. The 'transformer' part refers to the underlying architecture that makes modern AI so powerful.
Guardrails
Rules, filters, and safety mechanisms built into AI systems to prevent them from producing harmful, offensive, or inappropriate outputs. Guardrails are why AI assistants refuse certain requests. When building custom AI tools for businesses, we also add business-specific guardrails — for example, preventing a customer service bot from making promises the business can't keep.
Hallucination
When an AI confidently produces information that is factually incorrect or completely made up. This is one of the most important limitations of current AI tools to understand. AI doesn't 'know' things the way humans do — it predicts likely text, which can sometimes result in plausible-sounding but wrong answers. Always verify important facts.
Human-in-the-Loop
A system design where humans are involved in reviewing, approving, or correcting AI outputs before they're acted on. For most business applications, keeping a human in the loop is best practice — especially for anything customer-facing, financial, or legally sensitive.
Hyperparameter
Settings that control how an AI model is trained — like how fast it learns, how many training rounds it goes through, or how large its layers are. Unlike regular parameters (which the model learns automatically), hyperparameters are set by the people building the model. Getting them right is part of the craft of AI development.
Inference
The process of using a trained AI model to generate outputs from new inputs. When you type a question into ChatGPT and it responds, that's inference — the model is applying what it learned during training to your specific input. Training happens once; inference happens every time you use the tool.
Integration
Connecting two or more software systems so they can share data and work together. In AI, integration typically means connecting an AI tool to your existing business software — like linking a chatbot to your CRM, or connecting an automation tool to your booking system.
Jailbreak
An attempt to bypass the safety guidelines or restrictions built into an AI model — usually by crafting clever prompts that trick the AI into ignoring its rules. AI companies work continuously to prevent jailbreaks. As a business owner, it's worth knowing this exists so you understand why AI tools sometimes refuse certain requests — those guardrails are there for good reason.
Knowledge Base
A structured collection of information that an AI can draw on to answer questions. When we build a custom AI assistant for your business, we often create a knowledge base containing your FAQs, product details, policies, and procedures — so the AI gives accurate, business-specific answers rather than generic ones.
Large Language Model (LLM)
An AI model trained on enormous amounts of text data that can understand and generate human language. ChatGPT, Claude, and Gemini are all LLMs. They're called 'large' because of the sheer scale of data they're trained on and the number of parameters (internal settings) they contain — often in the billions.
Llama
Meta's family of open-source large language models, released for anyone to download, run, and build on. Unlike ChatGPT or Claude, Llama models can be run locally on your own hardware without sending data to a third-party server — making them popular for privacy-sensitive applications and businesses that want more control over their AI stack.
Machine Learning (ML)
A branch of AI where systems learn from data and improve their performance over time without being explicitly programmed for every scenario. Instead of following rigid rules, machine learning models identify patterns in data and use those patterns to make predictions or decisions.
Multimodal AI
AI that can process and generate multiple types of content — text, images, audio, and video — rather than just one. GPT-4o is multimodal: you can show it a photo and ask a question about it, or have it describe an image. Multimodal AI opens up a much wider range of business applications than text-only models.
Model
In AI, a model is the trained system that takes inputs and produces outputs. When people refer to 'the model', they mean the AI itself — the mathematical structure that has been trained on data. Different models have different strengths, weaknesses, and use cases.
Natural Language Processing (NLP)
The branch of AI focused on enabling computers to understand, interpret, and generate human language. NLP powers chatbots, voice assistants, translation tools, sentiment analysis, and much more. When an AI understands what you're asking in plain English, that's NLP at work.
Neural Network
A type of AI system loosely inspired by the structure of the human brain, made up of layers of interconnected nodes that process information. Neural networks are the foundation of most modern AI — including image recognition, speech recognition, and large language models.
No-Code / Low-Code
Tools and platforms that allow people to build software applications and automations without writing traditional code. Many AI tools are now available in no-code or low-code formats, making them accessible to business owners without technical backgrounds. Zapier and Make are popular examples.
OpenAI
The company behind ChatGPT, GPT-4, and DALL-E. OpenAI is one of the most influential AI research organizations in the world. Founded in 2015, it has become the most recognized name in consumer AI and provides the underlying technology for many third-party AI tools and applications.
Overfitting
When an AI model learns its training data too well — including the noise and quirks — and as a result performs poorly on new, real-world data. Think of it like a student who memorises past exam papers word-for-word but can't answer a question that's phrased slightly differently. Overfitting is a common challenge in building reliable AI systems.
Parameters
The internal settings of an AI model that are adjusted during training to improve performance. A model with more parameters is generally more capable but also more expensive to run. GPT-4 is estimated to have hundreds of billions of parameters — which is part of why it's so capable.
Prompt
The input you give to an AI model — the question, instruction, or context that tells the AI what you want it to do. Writing effective prompts (prompt engineering) is a skill that significantly affects the quality of AI outputs. A well-crafted prompt can be the difference between a mediocre and an excellent result.
Prompt Engineering
The practice of designing and refining prompts to get better outputs from AI models. It involves understanding how AI models interpret instructions and structuring your inputs to guide the model toward the response you want. Good prompt engineering is one of the most practical AI skills a business owner can develop.
Prompt Injection
A type of attack where a malicious user crafts inputs designed to override an AI system's instructions and make it behave in unintended ways. For example, a user might try to trick a customer service chatbot into ignoring its rules by embedding hidden instructions in their message. It's a real security concern for businesses deploying AI-facing tools — and something we account for when building AI systems for clients.
Query
A question or request submitted to an AI system, search engine, or database. In everyday AI use, a query is essentially what you type into ChatGPT or a search bar. In more technical contexts, queries are used to retrieve specific data from databases. The quality of your query directly affects the quality of the answer you get back.
RAG (Retrieval-Augmented Generation)
A technique that combines an AI language model with a search system, allowing the AI to retrieve relevant information from a specific knowledge base before generating a response. RAG is how you build AI tools that can answer questions about your specific business — your products, policies, FAQs — rather than just general knowledge.
Sentiment Analysis
An AI technique that identifies and categorises the emotional tone of text — positive, negative, or neutral. Businesses use sentiment analysis to monitor customer reviews, social media mentions, and support tickets to understand how customers feel about their products or services at scale.
Shadow AI
The use of AI tools by employees without the knowledge or approval of their organization's IT or management team. Just as 'shadow IT' described unauthorised software use, shadow AI is a growing concern — staff may be pasting sensitive client data into ChatGPT or using unapproved AI tools, creating data privacy and security risks. Having a clear AI policy in your business is increasingly important.
System Prompt
A set of instructions given to an AI model before a conversation begins, used to define its persona, rules, and behavior. When we build a custom AI chatbot for a business, the system prompt is where we tell it who it is, what it can and can't do, and how it should respond to customers.
Token
The basic unit of text that AI language models process. A token is roughly equivalent to a word or part of a word — 'hamburger' might be split into two tokens, while 'the' is one. AI models have token limits for inputs and outputs, and usage is often billed per token. Understanding tokens helps you use AI tools more efficiently.
Training
The process of teaching an AI model by exposing it to large amounts of data and adjusting its parameters to improve performance. Training a large AI model requires enormous computing power and can take weeks or months. Most businesses use pre-trained models rather than training their own from scratch.
Transformer
The underlying architecture that powers most modern AI language models, including GPT, Claude, and Gemini. Introduced by Google in 2017, the transformer architecture revolutionised AI by enabling models to process and understand context across long sequences of text far more effectively than previous approaches.
Use Case
A specific, practical application of a technology to solve a real problem. In AI, a use case might be 'using AI to automatically respond to common customer enquiries' or 'using AI to summarize meeting notes'. Identifying the right use cases for your business is the first step in any successful AI implementation.
Vector Database
A type of database designed to store and search data in the format that AI models use (vectors/embeddings). Vector databases are a key component of RAG systems — they allow AI tools to quickly find the most relevant information from a large knowledge base when answering a question.
Vibe Coding
A term coined in 2025 describing the practice of building software by describing what you want in plain English to an AI — and letting the AI write the code for you. Instead of learning to code, you 'vibe' with the AI: you describe the idea, review what it builds, give feedback, and iterate. Tools like Cursor, GitHub Copilot, and platforms like the one this website was built on make vibe coding accessible to non-developers. It's one of the most significant shifts in how software gets made.
Voice AI
AI systems that can understand and generate spoken language — enabling voice-based interactions with software. Voice AI powers smart speakers (Alexa, Siri, Google Assistant), AI phone receptionists, and voice-enabled customer service tools. For small businesses, voice AI can handle inbound calls, answer common questions, and route enquiries — 24 hours a day.
Workflow
A sequence of steps or tasks that make up a business process. In the context of AI and automation, a workflow is the series of actions that get triggered and executed automatically — for example, a new lead fills in a form, which triggers an AI to qualify them, send a welcome email, and add them to your CRM.
xAI
The AI company founded by Elon Musk in 2023, responsible for building Grok. xAI's stated mission is to understand the true nature of the universe through AI research. It's one of the newer major players in the AI space and is notable for its integration with the X (formerly Twitter) platform.
Yield (AI Output Quality)
In AI contexts, yield refers to the quality and usefulness of the outputs an AI produces relative to the inputs given. High-yield prompting means getting consistently valuable, accurate, and relevant responses. Improving your yield is largely about better prompts, clearer context, and choosing the right model for the task.
Zero-Shot Learning
The ability of an AI model to perform a task it hasn't been specifically trained on, using only a description or instruction. For example, asking ChatGPT to classify customer feedback into categories it's never seen before. Zero-shot capability is one of the things that makes modern LLMs so versatile for business use.
AI Washing
When a company exaggerates or falsely claims that its product uses AI to appear more innovative or attract investment. Similar to 'greenwashing' in sustainability. As AI becomes a buzzword, AI washing is increasingly common — it's worth asking vendors exactly how AI is used in their product, not just whether it is.
Algorithm
A set of step-by-step instructions a computer follows to complete a task or solve a problem. Think of it like a recipe — the computer follows each step in order to produce a result. Most AI tools are built on complex algorithms that process data and generate outputs.
Artificial Intelligence (AI)
Technology that enables computers to perform tasks that normally require human intelligence — things like understanding language, recognizing images, making decisions, and learning from experience. AI is an umbrella term that covers many different technologies and approaches.
Automation
Using technology to perform tasks with little or no human input. In a business context, automation means setting up systems that handle repetitive work automatically — like sending follow-up emails, updating spreadsheets, or routing customer enquiries — so your team can focus on higher-value work.
Agentic AI
AI that can take actions and make decisions autonomously to complete a goal — not just answer questions, but actually do things like browse the web, send emails, or interact with software on your behalf. Agentic AI is one of the fastest-growing areas of AI development.
AI-Native
A product, business, or workflow that is built from the ground up with AI at its core — rather than having AI bolted on as an afterthought. An AI-native business uses AI as a fundamental part of how it operates, not just as an occasional tool. Increasingly, being AI-native is a competitive advantage.
API (Application Programming Interface)
A way for two software applications to talk to each other. When we integrate an AI tool with your existing software, we're usually using APIs to connect them. You don't need to understand how APIs work — just know they're the plumbing that makes integrations possible.
ChatGPT
The AI assistant built by OpenAI and one of the most widely used AI tools in the world. ChatGPT can write, summarize, answer questions, draft emails, generate ideas, and much more — all through a simple chat interface. It runs on OpenAI's GPT models and is available free or via a paid plan. For most small businesses, it's the best starting point for exploring what AI can do.
Claude
An AI assistant built by Anthropic, widely regarded as one of the best alternatives to ChatGPT. Claude is known for nuanced writing, long-document analysis, and following complex instructions carefully. It's designed with a strong focus on safety and honesty. Available at claude.ai with a free tier and paid plans.
Bot
Short for 'robot' — a software program that performs automated tasks. In business, bots are commonly used for customer service (chatbots), data collection, and repetitive admin tasks. Not all bots use AI, but AI-powered bots are far more capable than simple rule-based ones.
Benchmark
A standardised test used to measure and compare the performance of AI models. When you see claims like 'our AI scores 90% on X benchmark', it means the model was tested against a standard set of problems. Benchmarks help compare different AI tools, though real-world performance can vary.
Black Box
A system where you can see the inputs and outputs but not what happens in between. Most AI models are black boxes — you give them a prompt and get a response, but the internal reasoning is not visible or easily explainable. This is why 'explainable AI' is an active area of research, particularly in regulated industries like finance and healthcare.
Chatbot
A software program designed to simulate conversation with humans, typically via text. Modern AI-powered chatbots (like those built on ChatGPT) can understand natural language, answer complex questions, and handle customer service tasks around the clock — far beyond the simple 'press 1 for billing' bots of the past.
Context Window
The amount of text an AI model can 'see' and consider at one time. Think of it like short-term memory — the larger the context window, the more of a conversation or document the AI can take into account when generating a response. Larger context windows generally produce more coherent, relevant outputs.
Copilot
An AI assistant that works alongside a human to help them do their job better — not replacing them, but augmenting their capabilities. Microsoft's Copilot (built into Office 365) is a well-known example. The term reflects the idea that the human is still in control; the AI is just helping navigate.
Data
Information in a form that computers can process. AI systems learn from data — the more relevant, high-quality data an AI is trained on, the better it tends to perform. In a business context, your data includes things like customer records, transaction history, emails, and documents.
Deepfake
AI-generated video, audio, or images that realistically depict someone saying or doing something they never actually said or did. Deepfakes are created using deep learning techniques and have become increasingly convincing. As a business owner, it's important to be aware of deepfakes — both as a potential security risk (e.g. fake audio of a CEO authorising a transfer) and as a reputational concern.
Deep Learning
A type of machine learning that uses neural networks with many layers to learn from large amounts of data. Deep learning powers many of the most impressive AI capabilities today — including image recognition, speech recognition, and large language models like ChatGPT.
Embedding
A way of representing text, images, or other data as numbers so that an AI can understand relationships between concepts. Embeddings allow AI to understand that 'dog' and 'puppy' are related, or that 'invoice' and 'bill' mean similar things — even without being explicitly told.
Emergent Behavior
Unexpected capabilities that appear in AI models as they grow larger or are trained on more data — abilities that weren't explicitly programmed or anticipated. For example, large language models spontaneously developed the ability to do basic arithmetic and translate languages without being specifically trained to do so. Emergent behavior is one of the reasons AI development can be unpredictable.
Fine-Tuning
The process of taking a pre-trained AI model and training it further on a specific dataset to make it better at a particular task. For example, fine-tuning a general language model on your company's documents and tone of voice so it writes in your style and understands your business context.
Frontier Model
The most advanced AI models available at any given time — the cutting edge of what's technically possible. GPT-4o, Claude 3.5 Sonnet, and Gemini Ultra are examples of frontier models. The term is used in AI policy and safety discussions to describe models powerful enough to potentially pose novel risks alongside their capabilities.
Foundation Model
A large AI model trained on a broad dataset that can be adapted for many different tasks. GPT-4, Claude, and Gemini are all foundation models. They're called 'foundation' models because they serve as the base that other, more specialized AI applications are built on top of.
Gemini
Google's flagship AI assistant and language model, available at gemini.google.com. Gemini integrates natively with Google Workspace — Gmail, Docs, Sheets, Drive — making it a strong choice for businesses already running on Google tools. It competes directly with ChatGPT and Claude and is available with a free tier and paid Google One AI Premium plan.
Grok
An AI assistant built by xAI, Elon Musk's AI company, and integrated into the X (formerly Twitter) platform. Grok is notable for having real-time access to information posted on X and for taking a less filtered approach to responses compared to some competitors. Available to X Premium subscribers and via the Grok standalone app.
Generative AI
AI that can create new content — text, images, audio, video, code — rather than just analysing or classifying existing content. ChatGPT generating a business proposal, DALL-E creating an image from a description, and GitHub Copilot writing code are all examples of generative AI in action.
GPT (Generative Pre-trained Transformer)
The type of AI model that powers ChatGPT, developed by OpenAI. GPT models are trained on vast amounts of text data and can generate human-like text, answer questions, summarize documents, write code, and much more. The 'transformer' part refers to the underlying architecture that makes modern AI so powerful.
Guardrails
Rules, filters, and safety mechanisms built into AI systems to prevent them from producing harmful, offensive, or inappropriate outputs. Guardrails are why AI assistants refuse certain requests. When building custom AI tools for businesses, we also add business-specific guardrails — for example, preventing a customer service bot from making promises the business can't keep.
Hallucination
When an AI confidently produces information that is factually incorrect or completely made up. This is one of the most important limitations of current AI tools to understand. AI doesn't 'know' things the way humans do — it predicts likely text, which can sometimes result in plausible-sounding but wrong answers. Always verify important facts.
Human-in-the-Loop
A system design where humans are involved in reviewing, approving, or correcting AI outputs before they're acted on. For most business applications, keeping a human in the loop is best practice — especially for anything customer-facing, financial, or legally sensitive.
Hyperparameter
Settings that control how an AI model is trained — like how fast it learns, how many training rounds it goes through, or how large its layers are. Unlike regular parameters (which the model learns automatically), hyperparameters are set by the people building the model. Getting them right is part of the craft of AI development.
Inference
The process of using a trained AI model to generate outputs from new inputs. When you type a question into ChatGPT and it responds, that's inference — the model is applying what it learned during training to your specific input. Training happens once; inference happens every time you use the tool.
Integration
Connecting two or more software systems so they can share data and work together. In AI, integration typically means connecting an AI tool to your existing business software — like linking a chatbot to your CRM, or connecting an automation tool to your booking system.
Jailbreak
An attempt to bypass the safety guidelines or restrictions built into an AI model — usually by crafting clever prompts that trick the AI into ignoring its rules. AI companies work continuously to prevent jailbreaks. As a business owner, it's worth knowing this exists so you understand why AI tools sometimes refuse certain requests — those guardrails are there for good reason.
Knowledge Base
A structured collection of information that an AI can draw on to answer questions. When we build a custom AI assistant for your business, we often create a knowledge base containing your FAQs, product details, policies, and procedures — so the AI gives accurate, business-specific answers rather than generic ones.
Large Language Model (LLM)
An AI model trained on enormous amounts of text data that can understand and generate human language. ChatGPT, Claude, and Gemini are all LLMs. They're called 'large' because of the sheer scale of data they're trained on and the number of parameters (internal settings) they contain — often in the billions.
Llama
Meta's family of open-source large language models, released for anyone to download, run, and build on. Unlike ChatGPT or Claude, Llama models can be run locally on your own hardware without sending data to a third-party server — making them popular for privacy-sensitive applications and businesses that want more control over their AI stack.
Machine Learning (ML)
A branch of AI where systems learn from data and improve their performance over time without being explicitly programmed for every scenario. Instead of following rigid rules, machine learning models identify patterns in data and use those patterns to make predictions or decisions.
Multimodal AI
AI that can process and generate multiple types of content — text, images, audio, and video — rather than just one. GPT-4o is multimodal: you can show it a photo and ask a question about it, or have it describe an image. Multimodal AI opens up a much wider range of business applications than text-only models.
Model
In AI, a model is the trained system that takes inputs and produces outputs. When people refer to 'the model', they mean the AI itself — the mathematical structure that has been trained on data. Different models have different strengths, weaknesses, and use cases.
Natural Language Processing (NLP)
The branch of AI focused on enabling computers to understand, interpret, and generate human language. NLP powers chatbots, voice assistants, translation tools, sentiment analysis, and much more. When an AI understands what you're asking in plain English, that's NLP at work.
Neural Network
A type of AI system loosely inspired by the structure of the human brain, made up of layers of interconnected nodes that process information. Neural networks are the foundation of most modern AI — including image recognition, speech recognition, and large language models.
No-Code / Low-Code
Tools and platforms that allow people to build software applications and automations without writing traditional code. Many AI tools are now available in no-code or low-code formats, making them accessible to business owners without technical backgrounds. Zapier and Make are popular examples.
OpenAI
The company behind ChatGPT, GPT-4, and DALL-E. OpenAI is one of the most influential AI research organizations in the world. Founded in 2015, it has become the most recognized name in consumer AI and provides the underlying technology for many third-party AI tools and applications.
Overfitting
When an AI model learns its training data too well — including the noise and quirks — and as a result performs poorly on new, real-world data. Think of it like a student who memorises past exam papers word-for-word but can't answer a question that's phrased slightly differently. Overfitting is a common challenge in building reliable AI systems.
Parameters
The internal settings of an AI model that are adjusted during training to improve performance. A model with more parameters is generally more capable but also more expensive to run. GPT-4 is estimated to have hundreds of billions of parameters — which is part of why it's so capable.
Prompt
The input you give to an AI model — the question, instruction, or context that tells the AI what you want it to do. Writing effective prompts (prompt engineering) is a skill that significantly affects the quality of AI outputs. A well-crafted prompt can be the difference between a mediocre and an excellent result.
Prompt Engineering
The practice of designing and refining prompts to get better outputs from AI models. It involves understanding how AI models interpret instructions and structuring your inputs to guide the model toward the response you want. Good prompt engineering is one of the most practical AI skills a business owner can develop.
Prompt Injection
A type of attack where a malicious user crafts inputs designed to override an AI system's instructions and make it behave in unintended ways. For example, a user might try to trick a customer service chatbot into ignoring its rules by embedding hidden instructions in their message. It's a real security concern for businesses deploying AI-facing tools — and something we account for when building AI systems for clients.
Query
A question or request submitted to an AI system, search engine, or database. In everyday AI use, a query is essentially what you type into ChatGPT or a search bar. In more technical contexts, queries are used to retrieve specific data from databases. The quality of your query directly affects the quality of the answer you get back.
RAG (Retrieval-Augmented Generation)
A technique that combines an AI language model with a search system, allowing the AI to retrieve relevant information from a specific knowledge base before generating a response. RAG is how you build AI tools that can answer questions about your specific business — your products, policies, FAQs — rather than just general knowledge.
Sentiment Analysis
An AI technique that identifies and categorises the emotional tone of text — positive, negative, or neutral. Businesses use sentiment analysis to monitor customer reviews, social media mentions, and support tickets to understand how customers feel about their products or services at scale.
Shadow AI
The use of AI tools by employees without the knowledge or approval of their organization's IT or management team. Just as 'shadow IT' described unauthorised software use, shadow AI is a growing concern — staff may be pasting sensitive client data into ChatGPT or using unapproved AI tools, creating data privacy and security risks. Having a clear AI policy in your business is increasingly important.
System Prompt
A set of instructions given to an AI model before a conversation begins, used to define its persona, rules, and behavior. When we build a custom AI chatbot for a business, the system prompt is where we tell it who it is, what it can and can't do, and how it should respond to customers.
Token
The basic unit of text that AI language models process. A token is roughly equivalent to a word or part of a word — 'hamburger' might be split into two tokens, while 'the' is one. AI models have token limits for inputs and outputs, and usage is often billed per token. Understanding tokens helps you use AI tools more efficiently.
Training
The process of teaching an AI model by exposing it to large amounts of data and adjusting its parameters to improve performance. Training a large AI model requires enormous computing power and can take weeks or months. Most businesses use pre-trained models rather than training their own from scratch.
Transformer
The underlying architecture that powers most modern AI language models, including GPT, Claude, and Gemini. Introduced by Google in 2017, the transformer architecture revolutionised AI by enabling models to process and understand context across long sequences of text far more effectively than previous approaches.
Use Case
A specific, practical application of a technology to solve a real problem. In AI, a use case might be 'using AI to automatically respond to common customer enquiries' or 'using AI to summarize meeting notes'. Identifying the right use cases for your business is the first step in any successful AI implementation.
Vector Database
A type of database designed to store and search data in the format that AI models use (vectors/embeddings). Vector databases are a key component of RAG systems — they allow AI tools to quickly find the most relevant information from a large knowledge base when answering a question.
Vibe Coding
A term coined in 2025 describing the practice of building software by describing what you want in plain English to an AI — and letting the AI write the code for you. Instead of learning to code, you 'vibe' with the AI: you describe the idea, review what it builds, give feedback, and iterate. Tools like Cursor, GitHub Copilot, and platforms like the one this website was built on make vibe coding accessible to non-developers. It's one of the most significant shifts in how software gets made.
Voice AI
AI systems that can understand and generate spoken language — enabling voice-based interactions with software. Voice AI powers smart speakers (Alexa, Siri, Google Assistant), AI phone receptionists, and voice-enabled customer service tools. For small businesses, voice AI can handle inbound calls, answer common questions, and route enquiries — 24 hours a day.
Workflow
A sequence of steps or tasks that make up a business process. In the context of AI and automation, a workflow is the series of actions that get triggered and executed automatically — for example, a new lead fills in a form, which triggers an AI to qualify them, send a welcome email, and add them to your CRM.
xAI
The AI company founded by Elon Musk in 2023, responsible for building Grok. xAI's stated mission is to understand the true nature of the universe through AI research. It's one of the newer major players in the AI space and is notable for its integration with the X (formerly Twitter) platform.
Yield (AI Output Quality)
In AI contexts, yield refers to the quality and usefulness of the outputs an AI produces relative to the inputs given. High-yield prompting means getting consistently valuable, accurate, and relevant responses. Improving your yield is largely about better prompts, clearer context, and choosing the right model for the task.
Zero-Shot Learning
The ability of an AI model to perform a task it hasn't been specifically trained on, using only a description or instruction. For example, asking ChatGPT to classify customer feedback into categories it's never seen before. Zero-shot capability is one of the things that makes modern LLMs so versatile for business use.
AI Washing
When a company exaggerates or falsely claims that its product uses AI to appear more innovative or attract investment. Similar to 'greenwashing' in sustainability. As AI becomes a buzzword, AI washing is increasingly common — it's worth asking vendors exactly how AI is used in their product, not just whether it is.
Algorithm
A set of step-by-step instructions a computer follows to complete a task or solve a problem. Think of it like a recipe — the computer follows each step in order to produce a result. Most AI tools are built on complex algorithms that process data and generate outputs.
Artificial Intelligence (AI)
Technology that enables computers to perform tasks that normally require human intelligence — things like understanding language, recognizing images, making decisions, and learning from experience. AI is an umbrella term that covers many different technologies and approaches.
Automation
Using technology to perform tasks with little or no human input. In a business context, automation means setting up systems that handle repetitive work automatically — like sending follow-up emails, updating spreadsheets, or routing customer enquiries — so your team can focus on higher-value work.
Agentic AI
AI that can take actions and make decisions autonomously to complete a goal — not just answer questions, but actually do things like browse the web, send emails, or interact with software on your behalf. Agentic AI is one of the fastest-growing areas of AI development.
AI-Native
A product, business, or workflow that is built from the ground up with AI at its core — rather than having AI bolted on as an afterthought. An AI-native business uses AI as a fundamental part of how it operates, not just as an occasional tool. Increasingly, being AI-native is a competitive advantage.
API (Application Programming Interface)
A way for two software applications to talk to each other. When we integrate an AI tool with your existing software, we're usually using APIs to connect them. You don't need to understand how APIs work — just know they're the plumbing that makes integrations possible.
ChatGPT
The AI assistant built by OpenAI and one of the most widely used AI tools in the world. ChatGPT can write, summarize, answer questions, draft emails, generate ideas, and much more — all through a simple chat interface. It runs on OpenAI's GPT models and is available free or via a paid plan. For most small businesses, it's the best starting point for exploring what AI can do.
Claude
An AI assistant built by Anthropic, widely regarded as one of the best alternatives to ChatGPT. Claude is known for nuanced writing, long-document analysis, and following complex instructions carefully. It's designed with a strong focus on safety and honesty. Available at claude.ai with a free tier and paid plans.
Bot
Short for 'robot' — a software program that performs automated tasks. In business, bots are commonly used for customer service (chatbots), data collection, and repetitive admin tasks. Not all bots use AI, but AI-powered bots are far more capable than simple rule-based ones.
Benchmark
A standardised test used to measure and compare the performance of AI models. When you see claims like 'our AI scores 90% on X benchmark', it means the model was tested against a standard set of problems. Benchmarks help compare different AI tools, though real-world performance can vary.
Black Box
A system where you can see the inputs and outputs but not what happens in between. Most AI models are black boxes — you give them a prompt and get a response, but the internal reasoning is not visible or easily explainable. This is why 'explainable AI' is an active area of research, particularly in regulated industries like finance and healthcare.
Chatbot
A software program designed to simulate conversation with humans, typically via text. Modern AI-powered chatbots (like those built on ChatGPT) can understand natural language, answer complex questions, and handle customer service tasks around the clock — far beyond the simple 'press 1 for billing' bots of the past.
Context Window
The amount of text an AI model can 'see' and consider at one time. Think of it like short-term memory — the larger the context window, the more of a conversation or document the AI can take into account when generating a response. Larger context windows generally produce more coherent, relevant outputs.
Copilot
An AI assistant that works alongside a human to help them do their job better — not replacing them, but augmenting their capabilities. Microsoft's Copilot (built into Office 365) is a well-known example. The term reflects the idea that the human is still in control; the AI is just helping navigate.
Data
Information in a form that computers can process. AI systems learn from data — the more relevant, high-quality data an AI is trained on, the better it tends to perform. In a business context, your data includes things like customer records, transaction history, emails, and documents.
Deepfake
AI-generated video, audio, or images that realistically depict someone saying or doing something they never actually said or did. Deepfakes are created using deep learning techniques and have become increasingly convincing. As a business owner, it's important to be aware of deepfakes — both as a potential security risk (e.g. fake audio of a CEO authorising a transfer) and as a reputational concern.
Deep Learning
A type of machine learning that uses neural networks with many layers to learn from large amounts of data. Deep learning powers many of the most impressive AI capabilities today — including image recognition, speech recognition, and large language models like ChatGPT.
Embedding
A way of representing text, images, or other data as numbers so that an AI can understand relationships between concepts. Embeddings allow AI to understand that 'dog' and 'puppy' are related, or that 'invoice' and 'bill' mean similar things — even without being explicitly told.
Emergent Behavior
Unexpected capabilities that appear in AI models as they grow larger or are trained on more data — abilities that weren't explicitly programmed or anticipated. For example, large language models spontaneously developed the ability to do basic arithmetic and translate languages without being specifically trained to do so. Emergent behavior is one of the reasons AI development can be unpredictable.
Fine-Tuning
The process of taking a pre-trained AI model and training it further on a specific dataset to make it better at a particular task. For example, fine-tuning a general language model on your company's documents and tone of voice so it writes in your style and understands your business context.
Frontier Model
The most advanced AI models available at any given time — the cutting edge of what's technically possible. GPT-4o, Claude 3.5 Sonnet, and Gemini Ultra are examples of frontier models. The term is used in AI policy and safety discussions to describe models powerful enough to potentially pose novel risks alongside their capabilities.
Foundation Model
A large AI model trained on a broad dataset that can be adapted for many different tasks. GPT-4, Claude, and Gemini are all foundation models. They're called 'foundation' models because they serve as the base that other, more specialized AI applications are built on top of.
Gemini
Google's flagship AI assistant and language model, available at gemini.google.com. Gemini integrates natively with Google Workspace — Gmail, Docs, Sheets, Drive — making it a strong choice for businesses already running on Google tools. It competes directly with ChatGPT and Claude and is available with a free tier and paid Google One AI Premium plan.
Grok
An AI assistant built by xAI, Elon Musk's AI company, and integrated into the X (formerly Twitter) platform. Grok is notable for having real-time access to information posted on X and for taking a less filtered approach to responses compared to some competitors. Available to X Premium subscribers and via the Grok standalone app.
Generative AI
AI that can create new content — text, images, audio, video, code — rather than just analysing or classifying existing content. ChatGPT generating a business proposal, DALL-E creating an image from a description, and GitHub Copilot writing code are all examples of generative AI in action.
GPT (Generative Pre-trained Transformer)
The type of AI model that powers ChatGPT, developed by OpenAI. GPT models are trained on vast amounts of text data and can generate human-like text, answer questions, summarize documents, write code, and much more. The 'transformer' part refers to the underlying architecture that makes modern AI so powerful.
Guardrails
Rules, filters, and safety mechanisms built into AI systems to prevent them from producing harmful, offensive, or inappropriate outputs. Guardrails are why AI assistants refuse certain requests. When building custom AI tools for businesses, we also add business-specific guardrails — for example, preventing a customer service bot from making promises the business can't keep.
Hallucination
When an AI confidently produces information that is factually incorrect or completely made up. This is one of the most important limitations of current AI tools to understand. AI doesn't 'know' things the way humans do — it predicts likely text, which can sometimes result in plausible-sounding but wrong answers. Always verify important facts.
Human-in-the-Loop
A system design where humans are involved in reviewing, approving, or correcting AI outputs before they're acted on. For most business applications, keeping a human in the loop is best practice — especially for anything customer-facing, financial, or legally sensitive.
Hyperparameter
Settings that control how an AI model is trained — like how fast it learns, how many training rounds it goes through, or how large its layers are. Unlike regular parameters (which the model learns automatically), hyperparameters are set by the people building the model. Getting them right is part of the craft of AI development.
Inference
The process of using a trained AI model to generate outputs from new inputs. When you type a question into ChatGPT and it responds, that's inference — the model is applying what it learned during training to your specific input. Training happens once; inference happens every time you use the tool.
Integration
Connecting two or more software systems so they can share data and work together. In AI, integration typically means connecting an AI tool to your existing business software — like linking a chatbot to your CRM, or connecting an automation tool to your booking system.
Jailbreak
An attempt to bypass the safety guidelines or restrictions built into an AI model — usually by crafting clever prompts that trick the AI into ignoring its rules. AI companies work continuously to prevent jailbreaks. As a business owner, it's worth knowing this exists so you understand why AI tools sometimes refuse certain requests — those guardrails are there for good reason.
Knowledge Base
A structured collection of information that an AI can draw on to answer questions. When we build a custom AI assistant for your business, we often create a knowledge base containing your FAQs, product details, policies, and procedures — so the AI gives accurate, business-specific answers rather than generic ones.
Large Language Model (LLM)
An AI model trained on enormous amounts of text data that can understand and generate human language. ChatGPT, Claude, and Gemini are all LLMs. They're called 'large' because of the sheer scale of data they're trained on and the number of parameters (internal settings) they contain — often in the billions.
Llama
Meta's family of open-source large language models, released for anyone to download, run, and build on. Unlike ChatGPT or Claude, Llama models can be run locally on your own hardware without sending data to a third-party server — making them popular for privacy-sensitive applications and businesses that want more control over their AI stack.
Machine Learning (ML)
A branch of AI where systems learn from data and improve their performance over time without being explicitly programmed for every scenario. Instead of following rigid rules, machine learning models identify patterns in data and use those patterns to make predictions or decisions.
Multimodal AI
AI that can process and generate multiple types of content — text, images, audio, and video — rather than just one. GPT-4o is multimodal: you can show it a photo and ask a question about it, or have it describe an image. Multimodal AI opens up a much wider range of business applications than text-only models.
Model
In AI, a model is the trained system that takes inputs and produces outputs. When people refer to 'the model', they mean the AI itself — the mathematical structure that has been trained on data. Different models have different strengths, weaknesses, and use cases.
Natural Language Processing (NLP)
The branch of AI focused on enabling computers to understand, interpret, and generate human language. NLP powers chatbots, voice assistants, translation tools, sentiment analysis, and much more. When an AI understands what you're asking in plain English, that's NLP at work.
Neural Network
A type of AI system loosely inspired by the structure of the human brain, made up of layers of interconnected nodes that process information. Neural networks are the foundation of most modern AI — including image recognition, speech recognition, and large language models.
No-Code / Low-Code
Tools and platforms that allow people to build software applications and automations without writing traditional code. Many AI tools are now available in no-code or low-code formats, making them accessible to business owners without technical backgrounds. Zapier and Make are popular examples.
OpenAI
The company behind ChatGPT, GPT-4, and DALL-E. OpenAI is one of the most influential AI research organizations in the world. Founded in 2015, it has become the most recognized name in consumer AI and provides the underlying technology for many third-party AI tools and applications.
Overfitting
When an AI model learns its training data too well — including the noise and quirks — and as a result performs poorly on new, real-world data. Think of it like a student who memorises past exam papers word-for-word but can't answer a question that's phrased slightly differently. Overfitting is a common challenge in building reliable AI systems.
Parameters
The internal settings of an AI model that are adjusted during training to improve performance. A model with more parameters is generally more capable but also more expensive to run. GPT-4 is estimated to have hundreds of billions of parameters — which is part of why it's so capable.
Prompt
The input you give to an AI model — the question, instruction, or context that tells the AI what you want it to do. Writing effective prompts (prompt engineering) is a skill that significantly affects the quality of AI outputs. A well-crafted prompt can be the difference between a mediocre and an excellent result.
Prompt Engineering
The practice of designing and refining prompts to get better outputs from AI models. It involves understanding how AI models interpret instructions and structuring your inputs to guide the model toward the response you want. Good prompt engineering is one of the most practical AI skills a business owner can develop.
Prompt Injection
A type of attack where a malicious user crafts inputs designed to override an AI system's instructions and make it behave in unintended ways. For example, a user might try to trick a customer service chatbot into ignoring its rules by embedding hidden instructions in their message. It's a real security concern for businesses deploying AI-facing tools — and something we account for when building AI systems for clients.
Query
A question or request submitted to an AI system, search engine, or database. In everyday AI use, a query is essentially what you type into ChatGPT or a search bar. In more technical contexts, queries are used to retrieve specific data from databases. The quality of your query directly affects the quality of the answer you get back.
RAG (Retrieval-Augmented Generation)
A technique that combines an AI language model with a search system, allowing the AI to retrieve relevant information from a specific knowledge base before generating a response. RAG is how you build AI tools that can answer questions about your specific business — your products, policies, FAQs — rather than just general knowledge.
Sentiment Analysis
An AI technique that identifies and categorises the emotional tone of text — positive, negative, or neutral. Businesses use sentiment analysis to monitor customer reviews, social media mentions, and support tickets to understand how customers feel about their products or services at scale.
Shadow AI
The use of AI tools by employees without the knowledge or approval of their organization's IT or management team. Just as 'shadow IT' described unauthorised software use, shadow AI is a growing concern — staff may be pasting sensitive client data into ChatGPT or using unapproved AI tools, creating data privacy and security risks. Having a clear AI policy in your business is increasingly important.
System Prompt
A set of instructions given to an AI model before a conversation begins, used to define its persona, rules, and behavior. When we build a custom AI chatbot for a business, the system prompt is where we tell it who it is, what it can and can't do, and how it should respond to customers.
Token
The basic unit of text that AI language models process. A token is roughly equivalent to a word or part of a word — 'hamburger' might be split into two tokens, while 'the' is one. AI models have token limits for inputs and outputs, and usage is often billed per token. Understanding tokens helps you use AI tools more efficiently.
Training
The process of teaching an AI model by exposing it to large amounts of data and adjusting its parameters to improve performance. Training a large AI model requires enormous computing power and can take weeks or months. Most businesses use pre-trained models rather than training their own from scratch.
Transformer
The underlying architecture that powers most modern AI language models, including GPT, Claude, and Gemini. Introduced by Google in 2017, the transformer architecture revolutionised AI by enabling models to process and understand context across long sequences of text far more effectively than previous approaches.
Use Case
A specific, practical application of a technology to solve a real problem. In AI, a use case might be 'using AI to automatically respond to common customer enquiries' or 'using AI to summarize meeting notes'. Identifying the right use cases for your business is the first step in any successful AI implementation.
Vector Database
A type of database designed to store and search data in the format that AI models use (vectors/embeddings). Vector databases are a key component of RAG systems — they allow AI tools to quickly find the most relevant information from a large knowledge base when answering a question.
Vibe Coding
A term coined in 2025 describing the practice of building software by describing what you want in plain English to an AI — and letting the AI write the code for you. Instead of learning to code, you 'vibe' with the AI: you describe the idea, review what it builds, give feedback, and iterate. Tools like Cursor, GitHub Copilot, and platforms like the one this website was built on make vibe coding accessible to non-developers. It's one of the most significant shifts in how software gets made.
Voice AI
AI systems that can understand and generate spoken language — enabling voice-based interactions with software. Voice AI powers smart speakers (Alexa, Siri, Google Assistant), AI phone receptionists, and voice-enabled customer service tools. For small businesses, voice AI can handle inbound calls, answer common questions, and route enquiries — 24 hours a day.
Workflow
A sequence of steps or tasks that make up a business process. In the context of AI and automation, a workflow is the series of actions that get triggered and executed automatically — for example, a new lead fills in a form, which triggers an AI to qualify them, send a welcome email, and add them to your CRM.
xAI
The AI company founded by Elon Musk in 2023, responsible for building Grok. xAI's stated mission is to understand the true nature of the universe through AI research. It's one of the newer major players in the AI space and is notable for its integration with the X (formerly Twitter) platform.
Yield (AI Output Quality)
In AI contexts, yield refers to the quality and usefulness of the outputs an AI produces relative to the inputs given. High-yield prompting means getting consistently valuable, accurate, and relevant responses. Improving your yield is largely about better prompts, clearer context, and choosing the right model for the task.
Zero-Shot Learning
The ability of an AI model to perform a task it hasn't been specifically trained on, using only a description or instruction. For example, asking ChatGPT to classify customer feedback into categories it's never seen before. Zero-shot capability is one of the things that makes modern LLMs so versatile for business use.
AI Washing
When a company exaggerates or falsely claims that its product uses AI to appear more innovative or attract investment. Similar to 'greenwashing' in sustainability. As AI becomes a buzzword, AI washing is increasingly common — it's worth asking vendors exactly how AI is used in their product, not just whether it is.
Algorithm
A set of step-by-step instructions a computer follows to complete a task or solve a problem. Think of it like a recipe — the computer follows each step in order to produce a result. Most AI tools are built on complex algorithms that process data and generate outputs.
Artificial Intelligence (AI)
Technology that enables computers to perform tasks that normally require human intelligence — things like understanding language, recognizing images, making decisions, and learning from experience. AI is an umbrella term that covers many different technologies and approaches.
Automation
Using technology to perform tasks with little or no human input. In a business context, automation means setting up systems that handle repetitive work automatically — like sending follow-up emails, updating spreadsheets, or routing customer enquiries — so your team can focus on higher-value work.
Agentic AI
AI that can take actions and make decisions autonomously to complete a goal — not just answer questions, but actually do things like browse the web, send emails, or interact with software on your behalf. Agentic AI is one of the fastest-growing areas of AI development.
AI-Native
A product, business, or workflow that is built from the ground up with AI at its core — rather than having AI bolted on as an afterthought. An AI-native business uses AI as a fundamental part of how it operates, not just as an occasional tool. Increasingly, being AI-native is a competitive advantage.
API (Application Programming Interface)
A way for two software applications to talk to each other. When we integrate an AI tool with your existing software, we're usually using APIs to connect them. You don't need to understand how APIs work — just know they're the plumbing that makes integrations possible.
ChatGPT
The AI assistant built by OpenAI and one of the most widely used AI tools in the world. ChatGPT can write, summarize, answer questions, draft emails, generate ideas, and much more — all through a simple chat interface. It runs on OpenAI's GPT models and is available free or via a paid plan. For most small businesses, it's the best starting point for exploring what AI can do.
Claude
An AI assistant built by Anthropic, widely regarded as one of the best alternatives to ChatGPT. Claude is known for nuanced writing, long-document analysis, and following complex instructions carefully. It's designed with a strong focus on safety and honesty. Available at claude.ai with a free tier and paid plans.
Bot
Short for 'robot' — a software program that performs automated tasks. In business, bots are commonly used for customer service (chatbots), data collection, and repetitive admin tasks. Not all bots use AI, but AI-powered bots are far more capable than simple rule-based ones.
Benchmark
A standardised test used to measure and compare the performance of AI models. When you see claims like 'our AI scores 90% on X benchmark', it means the model was tested against a standard set of problems. Benchmarks help compare different AI tools, though real-world performance can vary.
Black Box
A system where you can see the inputs and outputs but not what happens in between. Most AI models are black boxes — you give them a prompt and get a response, but the internal reasoning is not visible or easily explainable. This is why 'explainable AI' is an active area of research, particularly in regulated industries like finance and healthcare.
Chatbot
A software program designed to simulate conversation with humans, typically via text. Modern AI-powered chatbots (like those built on ChatGPT) can understand natural language, answer complex questions, and handle customer service tasks around the clock — far beyond the simple 'press 1 for billing' bots of the past.
Context Window
The amount of text an AI model can 'see' and consider at one time. Think of it like short-term memory — the larger the context window, the more of a conversation or document the AI can take into account when generating a response. Larger context windows generally produce more coherent, relevant outputs.
Copilot
An AI assistant that works alongside a human to help them do their job better — not replacing them, but augmenting their capabilities. Microsoft's Copilot (built into Office 365) is a well-known example. The term reflects the idea that the human is still in control; the AI is just helping navigate.
Data
Information in a form that computers can process. AI systems learn from data — the more relevant, high-quality data an AI is trained on, the better it tends to perform. In a business context, your data includes things like customer records, transaction history, emails, and documents.
Deepfake
AI-generated video, audio, or images that realistically depict someone saying or doing something they never actually said or did. Deepfakes are created using deep learning techniques and have become increasingly convincing. As a business owner, it's important to be aware of deepfakes — both as a potential security risk (e.g. fake audio of a CEO authorising a transfer) and as a reputational concern.
Deep Learning
A type of machine learning that uses neural networks with many layers to learn from large amounts of data. Deep learning powers many of the most impressive AI capabilities today — including image recognition, speech recognition, and large language models like ChatGPT.
Embedding
A way of representing text, images, or other data as numbers so that an AI can understand relationships between concepts. Embeddings allow AI to understand that 'dog' and 'puppy' are related, or that 'invoice' and 'bill' mean similar things — even without being explicitly told.
Emergent Behavior
Unexpected capabilities that appear in AI models as they grow larger or are trained on more data — abilities that weren't explicitly programmed or anticipated. For example, large language models spontaneously developed the ability to do basic arithmetic and translate languages without being specifically trained to do so. Emergent behavior is one of the reasons AI development can be unpredictable.
Fine-Tuning
The process of taking a pre-trained AI model and training it further on a specific dataset to make it better at a particular task. For example, fine-tuning a general language model on your company's documents and tone of voice so it writes in your style and understands your business context.
Frontier Model
The most advanced AI models available at any given time — the cutting edge of what's technically possible. GPT-4o, Claude 3.5 Sonnet, and Gemini Ultra are examples of frontier models. The term is used in AI policy and safety discussions to describe models powerful enough to potentially pose novel risks alongside their capabilities.
Foundation Model
A large AI model trained on a broad dataset that can be adapted for many different tasks. GPT-4, Claude, and Gemini are all foundation models. They're called 'foundation' models because they serve as the base that other, more specialized AI applications are built on top of.
Gemini
Google's flagship AI assistant and language model, available at gemini.google.com. Gemini integrates natively with Google Workspace — Gmail, Docs, Sheets, Drive — making it a strong choice for businesses already running on Google tools. It competes directly with ChatGPT and Claude and is available with a free tier and paid Google One AI Premium plan.
Grok
An AI assistant built by xAI, Elon Musk's AI company, and integrated into the X (formerly Twitter) platform. Grok is notable for having real-time access to information posted on X and for taking a less filtered approach to responses compared to some competitors. Available to X Premium subscribers and via the Grok standalone app.
Generative AI
AI that can create new content — text, images, audio, video, code — rather than just analysing or classifying existing content. ChatGPT generating a business proposal, DALL-E creating an image from a description, and GitHub Copilot writing code are all examples of generative AI in action.
GPT (Generative Pre-trained Transformer)
The type of AI model that powers ChatGPT, developed by OpenAI. GPT models are trained on vast amounts of text data and can generate human-like text, answer questions, summarize documents, write code, and much more. The 'transformer' part refers to the underlying architecture that makes modern AI so powerful.
Guardrails
Rules, filters, and safety mechanisms built into AI systems to prevent them from producing harmful, offensive, or inappropriate outputs. Guardrails are why AI assistants refuse certain requests. When building custom AI tools for businesses, we also add business-specific guardrails — for example, preventing a customer service bot from making promises the business can't keep.
Hallucination
When an AI confidently produces information that is factually incorrect or completely made up. This is one of the most important limitations of current AI tools to understand. AI doesn't 'know' things the way humans do — it predicts likely text, which can sometimes result in plausible-sounding but wrong answers. Always verify important facts.
Human-in-the-Loop
A system design where humans are involved in reviewing, approving, or correcting AI outputs before they're acted on. For most business applications, keeping a human in the loop is best practice — especially for anything customer-facing, financial, or legally sensitive.
Hyperparameter
Settings that control how an AI model is trained — like how fast it learns, how many training rounds it goes through, or how large its layers are. Unlike regular parameters (which the model learns automatically), hyperparameters are set by the people building the model. Getting them right is part of the craft of AI development.
Inference
The process of using a trained AI model to generate outputs from new inputs. When you type a question into ChatGPT and it responds, that's inference — the model is applying what it learned during training to your specific input. Training happens once; inference happens every time you use the tool.
Integration
Connecting two or more software systems so they can share data and work together. In AI, integration typically means connecting an AI tool to your existing business software — like linking a chatbot to your CRM, or connecting an automation tool to your booking system.
Jailbreak
An attempt to bypass the safety guidelines or restrictions built into an AI model — usually by crafting clever prompts that trick the AI into ignoring its rules. AI companies work continuously to prevent jailbreaks. As a business owner, it's worth knowing this exists so you understand why AI tools sometimes refuse certain requests — those guardrails are there for good reason.
Knowledge Base
A structured collection of information that an AI can draw on to answer questions. When we build a custom AI assistant for your business, we often create a knowledge base containing your FAQs, product details, policies, and procedures — so the AI gives accurate, business-specific answers rather than generic ones.
Large Language Model (LLM)
An AI model trained on enormous amounts of text data that can understand and generate human language. ChatGPT, Claude, and Gemini are all LLMs. They're called 'large' because of the sheer scale of data they're trained on and the number of parameters (internal settings) they contain — often in the billions.
Llama
Meta's family of open-source large language models, released for anyone to download, run, and build on. Unlike ChatGPT or Claude, Llama models can be run locally on your own hardware without sending data to a third-party server — making them popular for privacy-sensitive applications and businesses that want more control over their AI stack.
Machine Learning (ML)
A branch of AI where systems learn from data and improve their performance over time without being explicitly programmed for every scenario. Instead of following rigid rules, machine learning models identify patterns in data and use those patterns to make predictions or decisions.
Multimodal AI
AI that can process and generate multiple types of content — text, images, audio, and video — rather than just one. GPT-4o is multimodal: you can show it a photo and ask a question about it, or have it describe an image. Multimodal AI opens up a much wider range of business applications than text-only models.
Model
In AI, a model is the trained system that takes inputs and produces outputs. When people refer to 'the model', they mean the AI itself — the mathematical structure that has been trained on data. Different models have different strengths, weaknesses, and use cases.
Natural Language Processing (NLP)
The branch of AI focused on enabling computers to understand, interpret, and generate human language. NLP powers chatbots, voice assistants, translation tools, sentiment analysis, and much more. When an AI understands what you're asking in plain English, that's NLP at work.
Neural Network
A type of AI system loosely inspired by the structure of the human brain, made up of layers of interconnected nodes that process information. Neural networks are the foundation of most modern AI — including image recognition, speech recognition, and large language models.
No-Code / Low-Code
Tools and platforms that allow people to build software applications and automations without writing traditional code. Many AI tools are now available in no-code or low-code formats, making them accessible to business owners without technical backgrounds. Zapier and Make are popular examples.
OpenAI
The company behind ChatGPT, GPT-4, and DALL-E. OpenAI is one of the most influential AI research organizations in the world. Founded in 2015, it has become the most recognized name in consumer AI and provides the underlying technology for many third-party AI tools and applications.
Overfitting
When an AI model learns its training data too well — including the noise and quirks — and as a result performs poorly on new, real-world data. Think of it like a student who memorises past exam papers word-for-word but can't answer a question that's phrased slightly differently. Overfitting is a common challenge in building reliable AI systems.
Parameters
The internal settings of an AI model that are adjusted during training to improve performance. A model with more parameters is generally more capable but also more expensive to run. GPT-4 is estimated to have hundreds of billions of parameters — which is part of why it's so capable.
Prompt
The input you give to an AI model — the question, instruction, or context that tells the AI what you want it to do. Writing effective prompts (prompt engineering) is a skill that significantly affects the quality of AI outputs. A well-crafted prompt can be the difference between a mediocre and an excellent result.
Prompt Engineering
The practice of designing and refining prompts to get better outputs from AI models. It involves understanding how AI models interpret instructions and structuring your inputs to guide the model toward the response you want. Good prompt engineering is one of the most practical AI skills a business owner can develop.
Prompt Injection
A type of attack where a malicious user crafts inputs designed to override an AI system's instructions and make it behave in unintended ways. For example, a user might try to trick a customer service chatbot into ignoring its rules by embedding hidden instructions in their message. It's a real security concern for businesses deploying AI-facing tools — and something we account for when building AI systems for clients.
Query
A question or request submitted to an AI system, search engine, or database. In everyday AI use, a query is essentially what you type into ChatGPT or a search bar. In more technical contexts, queries are used to retrieve specific data from databases. The quality of your query directly affects the quality of the answer you get back.
RAG (Retrieval-Augmented Generation)
A technique that combines an AI language model with a search system, allowing the AI to retrieve relevant information from a specific knowledge base before generating a response. RAG is how you build AI tools that can answer questions about your specific business — your products, policies, FAQs — rather than just general knowledge.
Sentiment Analysis
An AI technique that identifies and categorises the emotional tone of text — positive, negative, or neutral. Businesses use sentiment analysis to monitor customer reviews, social media mentions, and support tickets to understand how customers feel about their products or services at scale.
Shadow AI
The use of AI tools by employees without the knowledge or approval of their organization's IT or management team. Just as 'shadow IT' described unauthorised software use, shadow AI is a growing concern — staff may be pasting sensitive client data into ChatGPT or using unapproved AI tools, creating data privacy and security risks. Having a clear AI policy in your business is increasingly important.
System Prompt
A set of instructions given to an AI model before a conversation begins, used to define its persona, rules, and behavior. When we build a custom AI chatbot for a business, the system prompt is where we tell it who it is, what it can and can't do, and how it should respond to customers.
Token
The basic unit of text that AI language models process. A token is roughly equivalent to a word or part of a word — 'hamburger' might be split into two tokens, while 'the' is one. AI models have token limits for inputs and outputs, and usage is often billed per token. Understanding tokens helps you use AI tools more efficiently.
Training
The process of teaching an AI model by exposing it to large amounts of data and adjusting its parameters to improve performance. Training a large AI model requires enormous computing power and can take weeks or months. Most businesses use pre-trained models rather than training their own from scratch.
Transformer
The underlying architecture that powers most modern AI language models, including GPT, Claude, and Gemini. Introduced by Google in 2017, the transformer architecture revolutionised AI by enabling models to process and understand context across long sequences of text far more effectively than previous approaches.
Use Case
A specific, practical application of a technology to solve a real problem. In AI, a use case might be 'using AI to automatically respond to common customer enquiries' or 'using AI to summarize meeting notes'. Identifying the right use cases for your business is the first step in any successful AI implementation.
Vector Database
A type of database designed to store and search data in the format that AI models use (vectors/embeddings). Vector databases are a key component of RAG systems — they allow AI tools to quickly find the most relevant information from a large knowledge base when answering a question.
Vibe Coding
A term coined in 2025 describing the practice of building software by describing what you want in plain English to an AI — and letting the AI write the code for you. Instead of learning to code, you 'vibe' with the AI: you describe the idea, review what it builds, give feedback, and iterate. Tools like Cursor, GitHub Copilot, and platforms like the one this website was built on make vibe coding accessible to non-developers. It's one of the most significant shifts in how software gets made.
Voice AI
AI systems that can understand and generate spoken language — enabling voice-based interactions with software. Voice AI powers smart speakers (Alexa, Siri, Google Assistant), AI phone receptionists, and voice-enabled customer service tools. For small businesses, voice AI can handle inbound calls, answer common questions, and route enquiries — 24 hours a day.
Workflow
A sequence of steps or tasks that make up a business process. In the context of AI and automation, a workflow is the series of actions that get triggered and executed automatically — for example, a new lead fills in a form, which triggers an AI to qualify them, send a welcome email, and add them to your CRM.
xAI
The AI company founded by Elon Musk in 2023, responsible for building Grok. xAI's stated mission is to understand the true nature of the universe through AI research. It's one of the newer major players in the AI space and is notable for its integration with the X (formerly Twitter) platform.
Yield (AI Output Quality)
In AI contexts, yield refers to the quality and usefulness of the outputs an AI produces relative to the inputs given. High-yield prompting means getting consistently valuable, accurate, and relevant responses. Improving your yield is largely about better prompts, clearer context, and choosing the right model for the task.
Zero-Shot Learning
The ability of an AI model to perform a task it hasn't been specifically trained on, using only a description or instruction. For example, asking ChatGPT to classify customer feedback into categories it's never seen before. Zero-shot capability is one of the things that makes modern LLMs so versatile for business use.
AI Washing
When a company exaggerates or falsely claims that its product uses AI to appear more innovative or attract investment. Similar to 'greenwashing' in sustainability. As AI becomes a buzzword, AI washing is increasingly common — it's worth asking vendors exactly how AI is used in their product, not just whether it is.
Algorithm
A set of step-by-step instructions a computer follows to complete a task or solve a problem. Think of it like a recipe — the computer follows each step in order to produce a result. Most AI tools are built on complex algorithms that process data and generate outputs.
Artificial Intelligence (AI)
Technology that enables computers to perform tasks that normally require human intelligence — things like understanding language, recognizing images, making decisions, and learning from experience. AI is an umbrella term that covers many different technologies and approaches.
Automation
Using technology to perform tasks with little or no human input. In a business context, automation means setting up systems that handle repetitive work automatically — like sending follow-up emails, updating spreadsheets, or routing customer enquiries — so your team can focus on higher-value work.
Agentic AI
AI that can take actions and make decisions autonomously to complete a goal — not just answer questions, but actually do things like browse the web, send emails, or interact with software on your behalf. Agentic AI is one of the fastest-growing areas of AI development.
AI-Native
A product, business, or workflow that is built from the ground up with AI at its core — rather than having AI bolted on as an afterthought. An AI-native business uses AI as a fundamental part of how it operates, not just as an occasional tool. Increasingly, being AI-native is a competitive advantage.
API (Application Programming Interface)
A way for two software applications to talk to each other. When we integrate an AI tool with your existing software, we're usually using APIs to connect them. You don't need to understand how APIs work — just know they're the plumbing that makes integrations possible.
ChatGPT
The AI assistant built by OpenAI and one of the most widely used AI tools in the world. ChatGPT can write, summarize, answer questions, draft emails, generate ideas, and much more — all through a simple chat interface. It runs on OpenAI's GPT models and is available free or via a paid plan. For most small businesses, it's the best starting point for exploring what AI can do.
Claude
An AI assistant built by Anthropic, widely regarded as one of the best alternatives to ChatGPT. Claude is known for nuanced writing, long-document analysis, and following complex instructions carefully. It's designed with a strong focus on safety and honesty. Available at claude.ai with a free tier and paid plans.
Bot
Short for 'robot' — a software program that performs automated tasks. In business, bots are commonly used for customer service (chatbots), data collection, and repetitive admin tasks. Not all bots use AI, but AI-powered bots are far more capable than simple rule-based ones.
Benchmark
A standardised test used to measure and compare the performance of AI models. When you see claims like 'our AI scores 90% on X benchmark', it means the model was tested against a standard set of problems. Benchmarks help compare different AI tools, though real-world performance can vary.
Black Box
A system where you can see the inputs and outputs but not what happens in between. Most AI models are black boxes — you give them a prompt and get a response, but the internal reasoning is not visible or easily explainable. This is why 'explainable AI' is an active area of research, particularly in regulated industries like finance and healthcare.
Chatbot
A software program designed to simulate conversation with humans, typically via text. Modern AI-powered chatbots (like those built on ChatGPT) can understand natural language, answer complex questions, and handle customer service tasks around the clock — far beyond the simple 'press 1 for billing' bots of the past.
Context Window
The amount of text an AI model can 'see' and consider at one time. Think of it like short-term memory — the larger the context window, the more of a conversation or document the AI can take into account when generating a response. Larger context windows generally produce more coherent, relevant outputs.
Copilot
An AI assistant that works alongside a human to help them do their job better — not replacing them, but augmenting their capabilities. Microsoft's Copilot (built into Office 365) is a well-known example. The term reflects the idea that the human is still in control; the AI is just helping navigate.
Data
Information in a form that computers can process. AI systems learn from data — the more relevant, high-quality data an AI is trained on, the better it tends to perform. In a business context, your data includes things like customer records, transaction history, emails, and documents.
Deepfake
AI-generated video, audio, or images that realistically depict someone saying or doing something they never actually said or did. Deepfakes are created using deep learning techniques and have become increasingly convincing. As a business owner, it's important to be aware of deepfakes — both as a potential security risk (e.g. fake audio of a CEO authorising a transfer) and as a reputational concern.
Deep Learning
A type of machine learning that uses neural networks with many layers to learn from large amounts of data. Deep learning powers many of the most impressive AI capabilities today — including image recognition, speech recognition, and large language models like ChatGPT.
Embedding
A way of representing text, images, or other data as numbers so that an AI can understand relationships between concepts. Embeddings allow AI to understand that 'dog' and 'puppy' are related, or that 'invoice' and 'bill' mean similar things — even without being explicitly told.
Emergent Behavior
Unexpected capabilities that appear in AI models as they grow larger or are trained on more data — abilities that weren't explicitly programmed or anticipated. For example, large language models spontaneously developed the ability to do basic arithmetic and translate languages without being specifically trained to do so. Emergent behavior is one of the reasons AI development can be unpredictable.
Fine-Tuning
The process of taking a pre-trained AI model and training it further on a specific dataset to make it better at a particular task. For example, fine-tuning a general language model on your company's documents and tone of voice so it writes in your style and understands your business context.
Frontier Model
The most advanced AI models available at any given time — the cutting edge of what's technically possible. GPT-4o, Claude 3.5 Sonnet, and Gemini Ultra are examples of frontier models. The term is used in AI policy and safety discussions to describe models powerful enough to potentially pose novel risks alongside their capabilities.
Foundation Model
A large AI model trained on a broad dataset that can be adapted for many different tasks. GPT-4, Claude, and Gemini are all foundation models. They're called 'foundation' models because they serve as the base that other, more specialized AI applications are built on top of.
Gemini
Google's flagship AI assistant and language model, available at gemini.google.com. Gemini integrates natively with Google Workspace — Gmail, Docs, Sheets, Drive — making it a strong choice for businesses already running on Google tools. It competes directly with ChatGPT and Claude and is available with a free tier and paid Google One AI Premium plan.
Grok
An AI assistant built by xAI, Elon Musk's AI company, and integrated into the X (formerly Twitter) platform. Grok is notable for having real-time access to information posted on X and for taking a less filtered approach to responses compared to some competitors. Available to X Premium subscribers and via the Grok standalone app.
Generative AI
AI that can create new content — text, images, audio, video, code — rather than just analysing or classifying existing content. ChatGPT generating a business proposal, DALL-E creating an image from a description, and GitHub Copilot writing code are all examples of generative AI in action.
GPT (Generative Pre-trained Transformer)
The type of AI model that powers ChatGPT, developed by OpenAI. GPT models are trained on vast amounts of text data and can generate human-like text, answer questions, summarize documents, write code, and much more. The 'transformer' part refers to the underlying architecture that makes modern AI so powerful.
Guardrails
Rules, filters, and safety mechanisms built into AI systems to prevent them from producing harmful, offensive, or inappropriate outputs. Guardrails are why AI assistants refuse certain requests. When building custom AI tools for businesses, we also add business-specific guardrails — for example, preventing a customer service bot from making promises the business can't keep.
Hallucination
When an AI confidently produces information that is factually incorrect or completely made up. This is one of the most important limitations of current AI tools to understand. AI doesn't 'know' things the way humans do — it predicts likely text, which can sometimes result in plausible-sounding but wrong answers. Always verify important facts.
Human-in-the-Loop
A system design where humans are involved in reviewing, approving, or correcting AI outputs before they're acted on. For most business applications, keeping a human in the loop is best practice — especially for anything customer-facing, financial, or legally sensitive.
Hyperparameter
Settings that control how an AI model is trained — like how fast it learns, how many training rounds it goes through, or how large its layers are. Unlike regular parameters (which the model learns automatically), hyperparameters are set by the people building the model. Getting them right is part of the craft of AI development.
Inference
The process of using a trained AI model to generate outputs from new inputs. When you type a question into ChatGPT and it responds, that's inference — the model is applying what it learned during training to your specific input. Training happens once; inference happens every time you use the tool.
Integration
Connecting two or more software systems so they can share data and work together. In AI, integration typically means connecting an AI tool to your existing business software — like linking a chatbot to your CRM, or connecting an automation tool to your booking system.
Jailbreak
An attempt to bypass the safety guidelines or restrictions built into an AI model — usually by crafting clever prompts that trick the AI into ignoring its rules. AI companies work continuously to prevent jailbreaks. As a business owner, it's worth knowing this exists so you understand why AI tools sometimes refuse certain requests — those guardrails are there for good reason.
Knowledge Base
A structured collection of information that an AI can draw on to answer questions. When we build a custom AI assistant for your business, we often create a knowledge base containing your FAQs, product details, policies, and procedures — so the AI gives accurate, business-specific answers rather than generic ones.
Large Language Model (LLM)
An AI model trained on enormous amounts of text data that can understand and generate human language. ChatGPT, Claude, and Gemini are all LLMs. They're called 'large' because of the sheer scale of data they're trained on and the number of parameters (internal settings) they contain — often in the billions.
Llama
Meta's family of open-source large language models, released for anyone to download, run, and build on. Unlike ChatGPT or Claude, Llama models can be run locally on your own hardware without sending data to a third-party server — making them popular for privacy-sensitive applications and businesses that want more control over their AI stack.
Machine Learning (ML)
A branch of AI where systems learn from data and improve their performance over time without being explicitly programmed for every scenario. Instead of following rigid rules, machine learning models identify patterns in data and use those patterns to make predictions or decisions.
Multimodal AI
AI that can process and generate multiple types of content — text, images, audio, and video — rather than just one. GPT-4o is multimodal: you can show it a photo and ask a question about it, or have it describe an image. Multimodal AI opens up a much wider range of business applications than text-only models.
Model
In AI, a model is the trained system that takes inputs and produces outputs. When people refer to 'the model', they mean the AI itself — the mathematical structure that has been trained on data. Different models have different strengths, weaknesses, and use cases.
Natural Language Processing (NLP)
The branch of AI focused on enabling computers to understand, interpret, and generate human language. NLP powers chatbots, voice assistants, translation tools, sentiment analysis, and much more. When an AI understands what you're asking in plain English, that's NLP at work.
Neural Network
A type of AI system loosely inspired by the structure of the human brain, made up of layers of interconnected nodes that process information. Neural networks are the foundation of most modern AI — including image recognition, speech recognition, and large language models.
No-Code / Low-Code
Tools and platforms that allow people to build software applications and automations without writing traditional code. Many AI tools are now available in no-code or low-code formats, making them accessible to business owners without technical backgrounds. Zapier and Make are popular examples.
OpenAI
The company behind ChatGPT, GPT-4, and DALL-E. OpenAI is one of the most influential AI research organizations in the world. Founded in 2015, it has become the most recognized name in consumer AI and provides the underlying technology for many third-party AI tools and applications.
Overfitting
When an AI model learns its training data too well — including the noise and quirks — and as a result performs poorly on new, real-world data. Think of it like a student who memorises past exam papers word-for-word but can't answer a question that's phrased slightly differently. Overfitting is a common challenge in building reliable AI systems.
Parameters
The internal settings of an AI model that are adjusted during training to improve performance. A model with more parameters is generally more capable but also more expensive to run. GPT-4 is estimated to have hundreds of billions of parameters — which is part of why it's so capable.
Prompt
The input you give to an AI model — the question, instruction, or context that tells the AI what you want it to do. Writing effective prompts (prompt engineering) is a skill that significantly affects the quality of AI outputs. A well-crafted prompt can be the difference between a mediocre and an excellent result.
Prompt Engineering
The practice of designing and refining prompts to get better outputs from AI models. It involves understanding how AI models interpret instructions and structuring your inputs to guide the model toward the response you want. Good prompt engineering is one of the most practical AI skills a business owner can develop.
Prompt Injection
A type of attack where a malicious user crafts inputs designed to override an AI system's instructions and make it behave in unintended ways. For example, a user might try to trick a customer service chatbot into ignoring its rules by embedding hidden instructions in their message. It's a real security concern for businesses deploying AI-facing tools — and something we account for when building AI systems for clients.
Query
A question or request submitted to an AI system, search engine, or database. In everyday AI use, a query is essentially what you type into ChatGPT or a search bar. In more technical contexts, queries are used to retrieve specific data from databases. The quality of your query directly affects the quality of the answer you get back.
RAG (Retrieval-Augmented Generation)
A technique that combines an AI language model with a search system, allowing the AI to retrieve relevant information from a specific knowledge base before generating a response. RAG is how you build AI tools that can answer questions about your specific business — your products, policies, FAQs — rather than just general knowledge.
Sentiment Analysis
An AI technique that identifies and categorises the emotional tone of text — positive, negative, or neutral. Businesses use sentiment analysis to monitor customer reviews, social media mentions, and support tickets to understand how customers feel about their products or services at scale.
Shadow AI
The use of AI tools by employees without the knowledge or approval of their organization's IT or management team. Just as 'shadow IT' described unauthorised software use, shadow AI is a growing concern — staff may be pasting sensitive client data into ChatGPT or using unapproved AI tools, creating data privacy and security risks. Having a clear AI policy in your business is increasingly important.
System Prompt
A set of instructions given to an AI model before a conversation begins, used to define its persona, rules, and behavior. When we build a custom AI chatbot for a business, the system prompt is where we tell it who it is, what it can and can't do, and how it should respond to customers.
Token
The basic unit of text that AI language models process. A token is roughly equivalent to a word or part of a word — 'hamburger' might be split into two tokens, while 'the' is one. AI models have token limits for inputs and outputs, and usage is often billed per token. Understanding tokens helps you use AI tools more efficiently.
Training
The process of teaching an AI model by exposing it to large amounts of data and adjusting its parameters to improve performance. Training a large AI model requires enormous computing power and can take weeks or months. Most businesses use pre-trained models rather than training their own from scratch.
Transformer
The underlying architecture that powers most modern AI language models, including GPT, Claude, and Gemini. Introduced by Google in 2017, the transformer architecture revolutionised AI by enabling models to process and understand context across long sequences of text far more effectively than previous approaches.
Use Case
A specific, practical application of a technology to solve a real problem. In AI, a use case might be 'using AI to automatically respond to common customer enquiries' or 'using AI to summarize meeting notes'. Identifying the right use cases for your business is the first step in any successful AI implementation.
Vector Database
A type of database designed to store and search data in the format that AI models use (vectors/embeddings). Vector databases are a key component of RAG systems — they allow AI tools to quickly find the most relevant information from a large knowledge base when answering a question.
Vibe Coding
A term coined in 2025 describing the practice of building software by describing what you want in plain English to an AI — and letting the AI write the code for you. Instead of learning to code, you 'vibe' with the AI: you describe the idea, review what it builds, give feedback, and iterate. Tools like Cursor, GitHub Copilot, and platforms like the one this website was built on make vibe coding accessible to non-developers. It's one of the most significant shifts in how software gets made.
Voice AI
AI systems that can understand and generate spoken language — enabling voice-based interactions with software. Voice AI powers smart speakers (Alexa, Siri, Google Assistant), AI phone receptionists, and voice-enabled customer service tools. For small businesses, voice AI can handle inbound calls, answer common questions, and route enquiries — 24 hours a day.
Workflow
A sequence of steps or tasks that make up a business process. In the context of AI and automation, a workflow is the series of actions that get triggered and executed automatically — for example, a new lead fills in a form, which triggers an AI to qualify them, send a welcome email, and add them to your CRM.
xAI
The AI company founded by Elon Musk in 2023, responsible for building Grok. xAI's stated mission is to understand the true nature of the universe through AI research. It's one of the newer major players in the AI space and is notable for its integration with the X (formerly Twitter) platform.
Yield (AI Output Quality)
In AI contexts, yield refers to the quality and usefulness of the outputs an AI produces relative to the inputs given. High-yield prompting means getting consistently valuable, accurate, and relevant responses. Improving your yield is largely about better prompts, clearer context, and choosing the right model for the task.
Zero-Shot Learning
The ability of an AI model to perform a task it hasn't been specifically trained on, using only a description or instruction. For example, asking ChatGPT to classify customer feedback into categories it's never seen before. Zero-shot capability is one of the things that makes modern LLMs so versatile for business use.
AI Washing
When a company exaggerates or falsely claims that its product uses AI to appear more innovative or attract investment. Similar to 'greenwashing' in sustainability. As AI becomes a buzzword, AI washing is increasingly common — it's worth asking vendors exactly how AI is used in their product, not just whether it is.
Algorithm
A set of step-by-step instructions a computer follows to complete a task or solve a problem. Think of it like a recipe — the computer follows each step in order to produce a result. Most AI tools are built on complex algorithms that process data and generate outputs.
Artificial Intelligence (AI)
Technology that enables computers to perform tasks that normally require human intelligence — things like understanding language, recognizing images, making decisions, and learning from experience. AI is an umbrella term that covers many different technologies and approaches.
Automation
Using technology to perform tasks with little or no human input. In a business context, automation means setting up systems that handle repetitive work automatically — like sending follow-up emails, updating spreadsheets, or routing customer enquiries — so your team can focus on higher-value work.
Agentic AI
AI that can take actions and make decisions autonomously to complete a goal — not just answer questions, but actually do things like browse the web, send emails, or interact with software on your behalf. Agentic AI is one of the fastest-growing areas of AI development.
AI-Native
A product, business, or workflow that is built from the ground up with AI at its core — rather than having AI bolted on as an afterthought. An AI-native business uses AI as a fundamental part of how it operates, not just as an occasional tool. Increasingly, being AI-native is a competitive advantage.
API (Application Programming Interface)
A way for two software applications to talk to each other. When we integrate an AI tool with your existing software, we're usually using APIs to connect them. You don't need to understand how APIs work — just know they're the plumbing that makes integrations possible.
ChatGPT
The AI assistant built by OpenAI and one of the most widely used AI tools in the world. ChatGPT can write, summarize, answer questions, draft emails, generate ideas, and much more — all through a simple chat interface. It runs on OpenAI's GPT models and is available free or via a paid plan. For most small businesses, it's the best starting point for exploring what AI can do.
Claude
An AI assistant built by Anthropic, widely regarded as one of the best alternatives to ChatGPT. Claude is known for nuanced writing, long-document analysis, and following complex instructions carefully. It's designed with a strong focus on safety and honesty. Available at claude.ai with a free tier and paid plans.
Bot
Short for 'robot' — a software program that performs automated tasks. In business, bots are commonly used for customer service (chatbots), data collection, and repetitive admin tasks. Not all bots use AI, but AI-powered bots are far more capable than simple rule-based ones.
Benchmark
A standardised test used to measure and compare the performance of AI models. When you see claims like 'our AI scores 90% on X benchmark', it means the model was tested against a standard set of problems. Benchmarks help compare different AI tools, though real-world performance can vary.
Black Box
A system where you can see the inputs and outputs but not what happens in between. Most AI models are black boxes — you give them a prompt and get a response, but the internal reasoning is not visible or easily explainable. This is why 'explainable AI' is an active area of research, particularly in regulated industries like finance and healthcare.
Chatbot
A software program designed to simulate conversation with humans, typically via text. Modern AI-powered chatbots (like those built on ChatGPT) can understand natural language, answer complex questions, and handle customer service tasks around the clock — far beyond the simple 'press 1 for billing' bots of the past.
Context Window
The amount of text an AI model can 'see' and consider at one time. Think of it like short-term memory — the larger the context window, the more of a conversation or document the AI can take into account when generating a response. Larger context windows generally produce more coherent, relevant outputs.
Copilot
An AI assistant that works alongside a human to help them do their job better — not replacing them, but augmenting their capabilities. Microsoft's Copilot (built into Office 365) is a well-known example. The term reflects the idea that the human is still in control; the AI is just helping navigate.
Data
Information in a form that computers can process. AI systems learn from data — the more relevant, high-quality data an AI is trained on, the better it tends to perform. In a business context, your data includes things like customer records, transaction history, emails, and documents.
Deepfake
AI-generated video, audio, or images that realistically depict someone saying or doing something they never actually said or did. Deepfakes are created using deep learning techniques and have become increasingly convincing. As a business owner, it's important to be aware of deepfakes — both as a potential security risk (e.g. fake audio of a CEO authorising a transfer) and as a reputational concern.
Deep Learning
A type of machine learning that uses neural networks with many layers to learn from large amounts of data. Deep learning powers many of the most impressive AI capabilities today — including image recognition, speech recognition, and large language models like ChatGPT.
Embedding
A way of representing text, images, or other data as numbers so that an AI can understand relationships between concepts. Embeddings allow AI to understand that 'dog' and 'puppy' are related, or that 'invoice' and 'bill' mean similar things — even without being explicitly told.
Emergent Behavior
Unexpected capabilities that appear in AI models as they grow larger or are trained on more data — abilities that weren't explicitly programmed or anticipated. For example, large language models spontaneously developed the ability to do basic arithmetic and translate languages without being specifically trained to do so. Emergent behavior is one of the reasons AI development can be unpredictable.
Fine-Tuning
The process of taking a pre-trained AI model and training it further on a specific dataset to make it better at a particular task. For example, fine-tuning a general language model on your company's documents and tone of voice so it writes in your style and understands your business context.
Frontier Model
The most advanced AI models available at any given time — the cutting edge of what's technically possible. GPT-4o, Claude 3.5 Sonnet, and Gemini Ultra are examples of frontier models. The term is used in AI policy and safety discussions to describe models powerful enough to potentially pose novel risks alongside their capabilities.
Foundation Model
A large AI model trained on a broad dataset that can be adapted for many different tasks. GPT-4, Claude, and Gemini are all foundation models. They're called 'foundation' models because they serve as the base that other, more specialized AI applications are built on top of.
Gemini
Google's flagship AI assistant and language model, available at gemini.google.com. Gemini integrates natively with Google Workspace — Gmail, Docs, Sheets, Drive — making it a strong choice for businesses already running on Google tools. It competes directly with ChatGPT and Claude and is available with a free tier and paid Google One AI Premium plan.
Grok
An AI assistant built by xAI, Elon Musk's AI company, and integrated into the X (formerly Twitter) platform. Grok is notable for having real-time access to information posted on X and for taking a less filtered approach to responses compared to some competitors. Available to X Premium subscribers and via the Grok standalone app.
Generative AI
AI that can create new content — text, images, audio, video, code — rather than just analysing or classifying existing content. ChatGPT generating a business proposal, DALL-E creating an image from a description, and GitHub Copilot writing code are all examples of generative AI in action.
GPT (Generative Pre-trained Transformer)
The type of AI model that powers ChatGPT, developed by OpenAI. GPT models are trained on vast amounts of text data and can generate human-like text, answer questions, summarize documents, write code, and much more. The 'transformer' part refers to the underlying architecture that makes modern AI so powerful.
Guardrails
Rules, filters, and safety mechanisms built into AI systems to prevent them from producing harmful, offensive, or inappropriate outputs. Guardrails are why AI assistants refuse certain requests. When building custom AI tools for businesses, we also add business-specific guardrails — for example, preventing a customer service bot from making promises the business can't keep.
Hallucination
When an AI confidently produces information that is factually incorrect or completely made up. This is one of the most important limitations of current AI tools to understand. AI doesn't 'know' things the way humans do — it predicts likely text, which can sometimes result in plausible-sounding but wrong answers. Always verify important facts.
Human-in-the-Loop
A system design where humans are involved in reviewing, approving, or correcting AI outputs before they're acted on. For most business applications, keeping a human in the loop is best practice — especially for anything customer-facing, financial, or legally sensitive.
Hyperparameter
Settings that control how an AI model is trained — like how fast it learns, how many training rounds it goes through, or how large its layers are. Unlike regular parameters (which the model learns automatically), hyperparameters are set by the people building the model. Getting them right is part of the craft of AI development.
Inference
The process of using a trained AI model to generate outputs from new inputs. When you type a question into ChatGPT and it responds, that's inference — the model is applying what it learned during training to your specific input. Training happens once; inference happens every time you use the tool.
Integration
Connecting two or more software systems so they can share data and work together. In AI, integration typically means connecting an AI tool to your existing business software — like linking a chatbot to your CRM, or connecting an automation tool to your booking system.
Jailbreak
An attempt to bypass the safety guidelines or restrictions built into an AI model — usually by crafting clever prompts that trick the AI into ignoring its rules. AI companies work continuously to prevent jailbreaks. As a business owner, it's worth knowing this exists so you understand why AI tools sometimes refuse certain requests — those guardrails are there for good reason.
Knowledge Base
A structured collection of information that an AI can draw on to answer questions. When we build a custom AI assistant for your business, we often create a knowledge base containing your FAQs, product details, policies, and procedures — so the AI gives accurate, business-specific answers rather than generic ones.
Large Language Model (LLM)
An AI model trained on enormous amounts of text data that can understand and generate human language. ChatGPT, Claude, and Gemini are all LLMs. They're called 'large' because of the sheer scale of data they're trained on and the number of parameters (internal settings) they contain — often in the billions.
Llama
Meta's family of open-source large language models, released for anyone to download, run, and build on. Unlike ChatGPT or Claude, Llama models can be run locally on your own hardware without sending data to a third-party server — making them popular for privacy-sensitive applications and businesses that want more control over their AI stack.
Machine Learning (ML)
A branch of AI where systems learn from data and improve their performance over time without being explicitly programmed for every scenario. Instead of following rigid rules, machine learning models identify patterns in data and use those patterns to make predictions or decisions.
Multimodal AI
AI that can process and generate multiple types of content — text, images, audio, and video — rather than just one. GPT-4o is multimodal: you can show it a photo and ask a question about it, or have it describe an image. Multimodal AI opens up a much wider range of business applications than text-only models.
Model
In AI, a model is the trained system that takes inputs and produces outputs. When people refer to 'the model', they mean the AI itself — the mathematical structure that has been trained on data. Different models have different strengths, weaknesses, and use cases.
Natural Language Processing (NLP)
The branch of AI focused on enabling computers to understand, interpret, and generate human language. NLP powers chatbots, voice assistants, translation tools, sentiment analysis, and much more. When an AI understands what you're asking in plain English, that's NLP at work.
Neural Network
A type of AI system loosely inspired by the structure of the human brain, made up of layers of interconnected nodes that process information. Neural networks are the foundation of most modern AI — including image recognition, speech recognition, and large language models.
No-Code / Low-Code
Tools and platforms that allow people to build software applications and automations without writing traditional code. Many AI tools are now available in no-code or low-code formats, making them accessible to business owners without technical backgrounds. Zapier and Make are popular examples.
OpenAI
The company behind ChatGPT, GPT-4, and DALL-E. OpenAI is one of the most influential AI research organizations in the world. Founded in 2015, it has become the most recognized name in consumer AI and provides the underlying technology for many third-party AI tools and applications.
Overfitting
When an AI model learns its training data too well — including the noise and quirks — and as a result performs poorly on new, real-world data. Think of it like a student who memorises past exam papers word-for-word but can't answer a question that's phrased slightly differently. Overfitting is a common challenge in building reliable AI systems.
Parameters
The internal settings of an AI model that are adjusted during training to improve performance. A model with more parameters is generally more capable but also more expensive to run. GPT-4 is estimated to have hundreds of billions of parameters — which is part of why it's so capable.
Prompt
The input you give to an AI model — the question, instruction, or context that tells the AI what you want it to do. Writing effective prompts (prompt engineering) is a skill that significantly affects the quality of AI outputs. A well-crafted prompt can be the difference between a mediocre and an excellent result.
Prompt Engineering
The practice of designing and refining prompts to get better outputs from AI models. It involves understanding how AI models interpret instructions and structuring your inputs to guide the model toward the response you want. Good prompt engineering is one of the most practical AI skills a business owner can develop.
Prompt Injection
A type of attack where a malicious user crafts inputs designed to override an AI system's instructions and make it behave in unintended ways. For example, a user might try to trick a customer service chatbot into ignoring its rules by embedding hidden instructions in their message. It's a real security concern for businesses deploying AI-facing tools — and something we account for when building AI systems for clients.
Query
A question or request submitted to an AI system, search engine, or database. In everyday AI use, a query is essentially what you type into ChatGPT or a search bar. In more technical contexts, queries are used to retrieve specific data from databases. The quality of your query directly affects the quality of the answer you get back.
RAG (Retrieval-Augmented Generation)
A technique that combines an AI language model with a search system, allowing the AI to retrieve relevant information from a specific knowledge base before generating a response. RAG is how you build AI tools that can answer questions about your specific business — your products, policies, FAQs — rather than just general knowledge.
Sentiment Analysis
An AI technique that identifies and categorises the emotional tone of text — positive, negative, or neutral. Businesses use sentiment analysis to monitor customer reviews, social media mentions, and support tickets to understand how customers feel about their products or services at scale.
Shadow AI
The use of AI tools by employees without the knowledge or approval of their organization's IT or management team. Just as 'shadow IT' described unauthorised software use, shadow AI is a growing concern — staff may be pasting sensitive client data into ChatGPT or using unapproved AI tools, creating data privacy and security risks. Having a clear AI policy in your business is increasingly important.
System Prompt
A set of instructions given to an AI model before a conversation begins, used to define its persona, rules, and behavior. When we build a custom AI chatbot for a business, the system prompt is where we tell it who it is, what it can and can't do, and how it should respond to customers.
Token
The basic unit of text that AI language models process. A token is roughly equivalent to a word or part of a word — 'hamburger' might be split into two tokens, while 'the' is one. AI models have token limits for inputs and outputs, and usage is often billed per token. Understanding tokens helps you use AI tools more efficiently.
Training
The process of teaching an AI model by exposing it to large amounts of data and adjusting its parameters to improve performance. Training a large AI model requires enormous computing power and can take weeks or months. Most businesses use pre-trained models rather than training their own from scratch.
Transformer
The underlying architecture that powers most modern AI language models, including GPT, Claude, and Gemini. Introduced by Google in 2017, the transformer architecture revolutionised AI by enabling models to process and understand context across long sequences of text far more effectively than previous approaches.
Use Case
A specific, practical application of a technology to solve a real problem. In AI, a use case might be 'using AI to automatically respond to common customer enquiries' or 'using AI to summarize meeting notes'. Identifying the right use cases for your business is the first step in any successful AI implementation.
Vector Database
A type of database designed to store and search data in the format that AI models use (vectors/embeddings). Vector databases are a key component of RAG systems — they allow AI tools to quickly find the most relevant information from a large knowledge base when answering a question.
Vibe Coding
A term coined in 2025 describing the practice of building software by describing what you want in plain English to an AI — and letting the AI write the code for you. Instead of learning to code, you 'vibe' with the AI: you describe the idea, review what it builds, give feedback, and iterate. Tools like Cursor, GitHub Copilot, and platforms like the one this website was built on make vibe coding accessible to non-developers. It's one of the most significant shifts in how software gets made.
Voice AI
AI systems that can understand and generate spoken language — enabling voice-based interactions with software. Voice AI powers smart speakers (Alexa, Siri, Google Assistant), AI phone receptionists, and voice-enabled customer service tools. For small businesses, voice AI can handle inbound calls, answer common questions, and route enquiries — 24 hours a day.
Workflow
A sequence of steps or tasks that make up a business process. In the context of AI and automation, a workflow is the series of actions that get triggered and executed automatically — for example, a new lead fills in a form, which triggers an AI to qualify them, send a welcome email, and add them to your CRM.
xAI
The AI company founded by Elon Musk in 2023, responsible for building Grok. xAI's stated mission is to understand the true nature of the universe through AI research. It's one of the newer major players in the AI space and is notable for its integration with the X (formerly Twitter) platform.
Yield (AI Output Quality)
In AI contexts, yield refers to the quality and usefulness of the outputs an AI produces relative to the inputs given. High-yield prompting means getting consistently valuable, accurate, and relevant responses. Improving your yield is largely about better prompts, clearer context, and choosing the right model for the task.
Zero-Shot Learning
The ability of an AI model to perform a task it hasn't been specifically trained on, using only a description or instruction. For example, asking ChatGPT to classify customer feedback into categories it's never seen before. Zero-shot capability is one of the things that makes modern LLMs so versatile for business use.
AI Washing
When a company exaggerates or falsely claims that its product uses AI to appear more innovative or attract investment. Similar to 'greenwashing' in sustainability. As AI becomes a buzzword, AI washing is increasingly common — it's worth asking vendors exactly how AI is used in their product, not just whether it is.
Algorithm
A set of step-by-step instructions a computer follows to complete a task or solve a problem. Think of it like a recipe — the computer follows each step in order to produce a result. Most AI tools are built on complex algorithms that process data and generate outputs.
Artificial Intelligence (AI)
Technology that enables computers to perform tasks that normally require human intelligence — things like understanding language, recognizing images, making decisions, and learning from experience. AI is an umbrella term that covers many different technologies and approaches.
Automation
Using technology to perform tasks with little or no human input. In a business context, automation means setting up systems that handle repetitive work automatically — like sending follow-up emails, updating spreadsheets, or routing customer enquiries — so your team can focus on higher-value work.
Agentic AI
AI that can take actions and make decisions autonomously to complete a goal — not just answer questions, but actually do things like browse the web, send emails, or interact with software on your behalf. Agentic AI is one of the fastest-growing areas of AI development.
AI-Native
A product, business, or workflow that is built from the ground up with AI at its core — rather than having AI bolted on as an afterthought. An AI-native business uses AI as a fundamental part of how it operates, not just as an occasional tool. Increasingly, being AI-native is a competitive advantage.
API (Application Programming Interface)
A way for two software applications to talk to each other. When we integrate an AI tool with your existing software, we're usually using APIs to connect them. You don't need to understand how APIs work — just know they're the plumbing that makes integrations possible.
ChatGPT
The AI assistant built by OpenAI and one of the most widely used AI tools in the world. ChatGPT can write, summarize, answer questions, draft emails, generate ideas, and much more — all through a simple chat interface. It runs on OpenAI's GPT models and is available free or via a paid plan. For most small businesses, it's the best starting point for exploring what AI can do.
Claude
An AI assistant built by Anthropic, widely regarded as one of the best alternatives to ChatGPT. Claude is known for nuanced writing, long-document analysis, and following complex instructions carefully. It's designed with a strong focus on safety and honesty. Available at claude.ai with a free tier and paid plans.
Bot
Short for 'robot' — a software program that performs automated tasks. In business, bots are commonly used for customer service (chatbots), data collection, and repetitive admin tasks. Not all bots use AI, but AI-powered bots are far more capable than simple rule-based ones.
Benchmark
A standardised test used to measure and compare the performance of AI models. When you see claims like 'our AI scores 90% on X benchmark', it means the model was tested against a standard set of problems. Benchmarks help compare different AI tools, though real-world performance can vary.
Black Box
A system where you can see the inputs and outputs but not what happens in between. Most AI models are black boxes — you give them a prompt and get a response, but the internal reasoning is not visible or easily explainable. This is why 'explainable AI' is an active area of research, particularly in regulated industries like finance and healthcare.
Chatbot
A software program designed to simulate conversation with humans, typically via text. Modern AI-powered chatbots (like those built on ChatGPT) can understand natural language, answer complex questions, and handle customer service tasks around the clock — far beyond the simple 'press 1 for billing' bots of the past.
Context Window
The amount of text an AI model can 'see' and consider at one time. Think of it like short-term memory — the larger the context window, the more of a conversation or document the AI can take into account when generating a response. Larger context windows generally produce more coherent, relevant outputs.
Copilot
An AI assistant that works alongside a human to help them do their job better — not replacing them, but augmenting their capabilities. Microsoft's Copilot (built into Office 365) is a well-known example. The term reflects the idea that the human is still in control; the AI is just helping navigate.
Data
Information in a form that computers can process. AI systems learn from data — the more relevant, high-quality data an AI is trained on, the better it tends to perform. In a business context, your data includes things like customer records, transaction history, emails, and documents.
Deepfake
AI-generated video, audio, or images that realistically depict someone saying or doing something they never actually said or did. Deepfakes are created using deep learning techniques and have become increasingly convincing. As a business owner, it's important to be aware of deepfakes — both as a potential security risk (e.g. fake audio of a CEO authorising a transfer) and as a reputational concern.
Deep Learning
A type of machine learning that uses neural networks with many layers to learn from large amounts of data. Deep learning powers many of the most impressive AI capabilities today — including image recognition, speech recognition, and large language models like ChatGPT.
Embedding
A way of representing text, images, or other data as numbers so that an AI can understand relationships between concepts. Embeddings allow AI to understand that 'dog' and 'puppy' are related, or that 'invoice' and 'bill' mean similar things — even without being explicitly told.
Emergent Behavior
Unexpected capabilities that appear in AI models as they grow larger or are trained on more data — abilities that weren't explicitly programmed or anticipated. For example, large language models spontaneously developed the ability to do basic arithmetic and translate languages without being specifically trained to do so. Emergent behavior is one of the reasons AI development can be unpredictable.
Fine-Tuning
The process of taking a pre-trained AI model and training it further on a specific dataset to make it better at a particular task. For example, fine-tuning a general language model on your company's documents and tone of voice so it writes in your style and understands your business context.
Frontier Model
The most advanced AI models available at any given time — the cutting edge of what's technically possible. GPT-4o, Claude 3.5 Sonnet, and Gemini Ultra are examples of frontier models. The term is used in AI policy and safety discussions to describe models powerful enough to potentially pose novel risks alongside their capabilities.
Foundation Model
A large AI model trained on a broad dataset that can be adapted for many different tasks. GPT-4, Claude, and Gemini are all foundation models. They're called 'foundation' models because they serve as the base that other, more specialized AI applications are built on top of.
Gemini
Google's flagship AI assistant and language model, available at gemini.google.com. Gemini integrates natively with Google Workspace — Gmail, Docs, Sheets, Drive — making it a strong choice for businesses already running on Google tools. It competes directly with ChatGPT and Claude and is available with a free tier and paid Google One AI Premium plan.
Grok
An AI assistant built by xAI, Elon Musk's AI company, and integrated into the X (formerly Twitter) platform. Grok is notable for having real-time access to information posted on X and for taking a less filtered approach to responses compared to some competitors. Available to X Premium subscribers and via the Grok standalone app.
Generative AI
AI that can create new content — text, images, audio, video, code — rather than just analysing or classifying existing content. ChatGPT generating a business proposal, DALL-E creating an image from a description, and GitHub Copilot writing code are all examples of generative AI in action.
GPT (Generative Pre-trained Transformer)
The type of AI model that powers ChatGPT, developed by OpenAI. GPT models are trained on vast amounts of text data and can generate human-like text, answer questions, summarize documents, write code, and much more. The 'transformer' part refers to the underlying architecture that makes modern AI so powerful.
Guardrails
Rules, filters, and safety mechanisms built into AI systems to prevent them from producing harmful, offensive, or inappropriate outputs. Guardrails are why AI assistants refuse certain requests. When building custom AI tools for businesses, we also add business-specific guardrails — for example, preventing a customer service bot from making promises the business can't keep.
Hallucination
When an AI confidently produces information that is factually incorrect or completely made up. This is one of the most important limitations of current AI tools to understand. AI doesn't 'know' things the way humans do — it predicts likely text, which can sometimes result in plausible-sounding but wrong answers. Always verify important facts.
Human-in-the-Loop
A system design where humans are involved in reviewing, approving, or correcting AI outputs before they're acted on. For most business applications, keeping a human in the loop is best practice — especially for anything customer-facing, financial, or legally sensitive.
Hyperparameter
Settings that control how an AI model is trained — like how fast it learns, how many training rounds it goes through, or how large its layers are. Unlike regular parameters (which the model learns automatically), hyperparameters are set by the people building the model. Getting them right is part of the craft of AI development.
Inference
The process of using a trained AI model to generate outputs from new inputs. When you type a question into ChatGPT and it responds, that's inference — the model is applying what it learned during training to your specific input. Training happens once; inference happens every time you use the tool.
Integration
Connecting two or more software systems so they can share data and work together. In AI, integration typically means connecting an AI tool to your existing business software — like linking a chatbot to your CRM, or connecting an automation tool to your booking system.
Jailbreak
An attempt to bypass the safety guidelines or restrictions built into an AI model — usually by crafting clever prompts that trick the AI into ignoring its rules. AI companies work continuously to prevent jailbreaks. As a business owner, it's worth knowing this exists so you understand why AI tools sometimes refuse certain requests — those guardrails are there for good reason.
Knowledge Base
A structured collection of information that an AI can draw on to answer questions. When we build a custom AI assistant for your business, we often create a knowledge base containing your FAQs, product details, policies, and procedures — so the AI gives accurate, business-specific answers rather than generic ones.
Large Language Model (LLM)
An AI model trained on enormous amounts of text data that can understand and generate human language. ChatGPT, Claude, and Gemini are all LLMs. They're called 'large' because of the sheer scale of data they're trained on and the number of parameters (internal settings) they contain — often in the billions.
Llama
Meta's family of open-source large language models, released for anyone to download, run, and build on. Unlike ChatGPT or Claude, Llama models can be run locally on your own hardware without sending data to a third-party server — making them popular for privacy-sensitive applications and businesses that want more control over their AI stack.
Machine Learning (ML)
A branch of AI where systems learn from data and improve their performance over time without being explicitly programmed for every scenario. Instead of following rigid rules, machine learning models identify patterns in data and use those patterns to make predictions or decisions.
Multimodal AI
AI that can process and generate multiple types of content — text, images, audio, and video — rather than just one. GPT-4o is multimodal: you can show it a photo and ask a question about it, or have it describe an image. Multimodal AI opens up a much wider range of business applications than text-only models.
Model
In AI, a model is the trained system that takes inputs and produces outputs. When people refer to 'the model', they mean the AI itself — the mathematical structure that has been trained on data. Different models have different strengths, weaknesses, and use cases.
Natural Language Processing (NLP)
The branch of AI focused on enabling computers to understand, interpret, and generate human language. NLP powers chatbots, voice assistants, translation tools, sentiment analysis, and much more. When an AI understands what you're asking in plain English, that's NLP at work.
Neural Network
A type of AI system loosely inspired by the structure of the human brain, made up of layers of interconnected nodes that process information. Neural networks are the foundation of most modern AI — including image recognition, speech recognition, and large language models.
No-Code / Low-Code
Tools and platforms that allow people to build software applications and automations without writing traditional code. Many AI tools are now available in no-code or low-code formats, making them accessible to business owners without technical backgrounds. Zapier and Make are popular examples.
OpenAI
The company behind ChatGPT, GPT-4, and DALL-E. OpenAI is one of the most influential AI research organizations in the world. Founded in 2015, it has become the most recognized name in consumer AI and provides the underlying technology for many third-party AI tools and applications.
Overfitting
When an AI model learns its training data too well — including the noise and quirks — and as a result performs poorly on new, real-world data. Think of it like a student who memorises past exam papers word-for-word but can't answer a question that's phrased slightly differently. Overfitting is a common challenge in building reliable AI systems.
Parameters
The internal settings of an AI model that are adjusted during training to improve performance. A model with more parameters is generally more capable but also more expensive to run. GPT-4 is estimated to have hundreds of billions of parameters — which is part of why it's so capable.
Prompt
The input you give to an AI model — the question, instruction, or context that tells the AI what you want it to do. Writing effective prompts (prompt engineering) is a skill that significantly affects the quality of AI outputs. A well-crafted prompt can be the difference between a mediocre and an excellent result.
Prompt Engineering
The practice of designing and refining prompts to get better outputs from AI models. It involves understanding how AI models interpret instructions and structuring your inputs to guide the model toward the response you want. Good prompt engineering is one of the most practical AI skills a business owner can develop.
Prompt Injection
A type of attack where a malicious user crafts inputs designed to override an AI system's instructions and make it behave in unintended ways. For example, a user might try to trick a customer service chatbot into ignoring its rules by embedding hidden instructions in their message. It's a real security concern for businesses deploying AI-facing tools — and something we account for when building AI systems for clients.
Query
A question or request submitted to an AI system, search engine, or database. In everyday AI use, a query is essentially what you type into ChatGPT or a search bar. In more technical contexts, queries are used to retrieve specific data from databases. The quality of your query directly affects the quality of the answer you get back.
RAG (Retrieval-Augmented Generation)
A technique that combines an AI language model with a search system, allowing the AI to retrieve relevant information from a specific knowledge base before generating a response. RAG is how you build AI tools that can answer questions about your specific business — your products, policies, FAQs — rather than just general knowledge.
Sentiment Analysis
An AI technique that identifies and categorises the emotional tone of text — positive, negative, or neutral. Businesses use sentiment analysis to monitor customer reviews, social media mentions, and support tickets to understand how customers feel about their products or services at scale.
Shadow AI
The use of AI tools by employees without the knowledge or approval of their organization's IT or management team. Just as 'shadow IT' described unauthorised software use, shadow AI is a growing concern — staff may be pasting sensitive client data into ChatGPT or using unapproved AI tools, creating data privacy and security risks. Having a clear AI policy in your business is increasingly important.
System Prompt
A set of instructions given to an AI model before a conversation begins, used to define its persona, rules, and behavior. When we build a custom AI chatbot for a business, the system prompt is where we tell it who it is, what it can and can't do, and how it should respond to customers.
Token
The basic unit of text that AI language models process. A token is roughly equivalent to a word or part of a word — 'hamburger' might be split into two tokens, while 'the' is one. AI models have token limits for inputs and outputs, and usage is often billed per token. Understanding tokens helps you use AI tools more efficiently.
Training
The process of teaching an AI model by exposing it to large amounts of data and adjusting its parameters to improve performance. Training a large AI model requires enormous computing power and can take weeks or months. Most businesses use pre-trained models rather than training their own from scratch.
Transformer
The underlying architecture that powers most modern AI language models, including GPT, Claude, and Gemini. Introduced by Google in 2017, the transformer architecture revolutionised AI by enabling models to process and understand context across long sequences of text far more effectively than previous approaches.
Use Case
A specific, practical application of a technology to solve a real problem. In AI, a use case might be 'using AI to automatically respond to common customer enquiries' or 'using AI to summarize meeting notes'. Identifying the right use cases for your business is the first step in any successful AI implementation.
Vector Database
A type of database designed to store and search data in the format that AI models use (vectors/embeddings). Vector databases are a key component of RAG systems — they allow AI tools to quickly find the most relevant information from a large knowledge base when answering a question.
Vibe Coding
A term coined in 2025 describing the practice of building software by describing what you want in plain English to an AI — and letting the AI write the code for you. Instead of learning to code, you 'vibe' with the AI: you describe the idea, review what it builds, give feedback, and iterate. Tools like Cursor, GitHub Copilot, and platforms like the one this website was built on make vibe coding accessible to non-developers. It's one of the most significant shifts in how software gets made.
Voice AI
AI systems that can understand and generate spoken language — enabling voice-based interactions with software. Voice AI powers smart speakers (Alexa, Siri, Google Assistant), AI phone receptionists, and voice-enabled customer service tools. For small businesses, voice AI can handle inbound calls, answer common questions, and route enquiries — 24 hours a day.
Workflow
A sequence of steps or tasks that make up a business process. In the context of AI and automation, a workflow is the series of actions that get triggered and executed automatically — for example, a new lead fills in a form, which triggers an AI to qualify them, send a welcome email, and add them to your CRM.
xAI
The AI company founded by Elon Musk in 2023, responsible for building Grok. xAI's stated mission is to understand the true nature of the universe through AI research. It's one of the newer major players in the AI space and is notable for its integration with the X (formerly Twitter) platform.
Yield (AI Output Quality)
In AI contexts, yield refers to the quality and usefulness of the outputs an AI produces relative to the inputs given. High-yield prompting means getting consistently valuable, accurate, and relevant responses. Improving your yield is largely about better prompts, clearer context, and choosing the right model for the task.
Zero-Shot Learning
The ability of an AI model to perform a task it hasn't been specifically trained on, using only a description or instruction. For example, asking ChatGPT to classify customer feedback into categories it's never seen before. Zero-shot capability is one of the things that makes modern LLMs so versatile for business use.
AI Washing
When a company exaggerates or falsely claims that its product uses AI to appear more innovative or attract investment. Similar to 'greenwashing' in sustainability. As AI becomes a buzzword, AI washing is increasingly common — it's worth asking vendors exactly how AI is used in their product, not just whether it is.
Algorithm
A set of step-by-step instructions a computer follows to complete a task or solve a problem. Think of it like a recipe — the computer follows each step in order to produce a result. Most AI tools are built on complex algorithms that process data and generate outputs.
Artificial Intelligence (AI)
Technology that enables computers to perform tasks that normally require human intelligence — things like understanding language, recognizing images, making decisions, and learning from experience. AI is an umbrella term that covers many different technologies and approaches.
Automation
Using technology to perform tasks with little or no human input. In a business context, automation means setting up systems that handle repetitive work automatically — like sending follow-up emails, updating spreadsheets, or routing customer enquiries — so your team can focus on higher-value work.
Agentic AI
AI that can take actions and make decisions autonomously to complete a goal — not just answer questions, but actually do things like browse the web, send emails, or interact with software on your behalf. Agentic AI is one of the fastest-growing areas of AI development.
AI-Native
A product, business, or workflow that is built from the ground up with AI at its core — rather than having AI bolted on as an afterthought. An AI-native business uses AI as a fundamental part of how it operates, not just as an occasional tool. Increasingly, being AI-native is a competitive advantage.
API (Application Programming Interface)
A way for two software applications to talk to each other. When we integrate an AI tool with your existing software, we're usually using APIs to connect them. You don't need to understand how APIs work — just know they're the plumbing that makes integrations possible.
ChatGPT
The AI assistant built by OpenAI and one of the most widely used AI tools in the world. ChatGPT can write, summarize, answer questions, draft emails, generate ideas, and much more — all through a simple chat interface. It runs on OpenAI's GPT models and is available free or via a paid plan. For most small businesses, it's the best starting point for exploring what AI can do.
Claude
An AI assistant built by Anthropic, widely regarded as one of the best alternatives to ChatGPT. Claude is known for nuanced writing, long-document analysis, and following complex instructions carefully. It's designed with a strong focus on safety and honesty. Available at claude.ai with a free tier and paid plans.
Bot
Short for 'robot' — a software program that performs automated tasks. In business, bots are commonly used for customer service (chatbots), data collection, and repetitive admin tasks. Not all bots use AI, but AI-powered bots are far more capable than simple rule-based ones.
Benchmark
A standardised test used to measure and compare the performance of AI models. When you see claims like 'our AI scores 90% on X benchmark', it means the model was tested against a standard set of problems. Benchmarks help compare different AI tools, though real-world performance can vary.
Black Box
A system where you can see the inputs and outputs but not what happens in between. Most AI models are black boxes — you give them a prompt and get a response, but the internal reasoning is not visible or easily explainable. This is why 'explainable AI' is an active area of research, particularly in regulated industries like finance and healthcare.
Chatbot
A software program designed to simulate conversation with humans, typically via text. Modern AI-powered chatbots (like those built on ChatGPT) can understand natural language, answer complex questions, and handle customer service tasks around the clock — far beyond the simple 'press 1 for billing' bots of the past.
Context Window
The amount of text an AI model can 'see' and consider at one time. Think of it like short-term memory — the larger the context window, the more of a conversation or document the AI can take into account when generating a response. Larger context windows generally produce more coherent, relevant outputs.
Copilot
An AI assistant that works alongside a human to help them do their job better — not replacing them, but augmenting their capabilities. Microsoft's Copilot (built into Office 365) is a well-known example. The term reflects the idea that the human is still in control; the AI is just helping navigate.
Data
Information in a form that computers can process. AI systems learn from data — the more relevant, high-quality data an AI is trained on, the better it tends to perform. In a business context, your data includes things like customer records, transaction history, emails, and documents.
Deepfake
AI-generated video, audio, or images that realistically depict someone saying or doing something they never actually said or did. Deepfakes are created using deep learning techniques and have become increasingly convincing. As a business owner, it's important to be aware of deepfakes — both as a potential security risk (e.g. fake audio of a CEO authorising a transfer) and as a reputational concern.
Deep Learning
A type of machine learning that uses neural networks with many layers to learn from large amounts of data. Deep learning powers many of the most impressive AI capabilities today — including image recognition, speech recognition, and large language models like ChatGPT.
Embedding
A way of representing text, images, or other data as numbers so that an AI can understand relationships between concepts. Embeddings allow AI to understand that 'dog' and 'puppy' are related, or that 'invoice' and 'bill' mean similar things — even without being explicitly told.
Emergent Behavior
Unexpected capabilities that appear in AI models as they grow larger or are trained on more data — abilities that weren't explicitly programmed or anticipated. For example, large language models spontaneously developed the ability to do basic arithmetic and translate languages without being specifically trained to do so. Emergent behavior is one of the reasons AI development can be unpredictable.
Fine-Tuning
The process of taking a pre-trained AI model and training it further on a specific dataset to make it better at a particular task. For example, fine-tuning a general language model on your company's documents and tone of voice so it writes in your style and understands your business context.
Frontier Model
The most advanced AI models available at any given time — the cutting edge of what's technically possible. GPT-4o, Claude 3.5 Sonnet, and Gemini Ultra are examples of frontier models. The term is used in AI policy and safety discussions to describe models powerful enough to potentially pose novel risks alongside their capabilities.
Foundation Model
A large AI model trained on a broad dataset that can be adapted for many different tasks. GPT-4, Claude, and Gemini are all foundation models. They're called 'foundation' models because they serve as the base that other, more specialized AI applications are built on top of.
Gemini
Google's flagship AI assistant and language model, available at gemini.google.com. Gemini integrates natively with Google Workspace — Gmail, Docs, Sheets, Drive — making it a strong choice for businesses already running on Google tools. It competes directly with ChatGPT and Claude and is available with a free tier and paid Google One AI Premium plan.
Grok
An AI assistant built by xAI, Elon Musk's AI company, and integrated into the X (formerly Twitter) platform. Grok is notable for having real-time access to information posted on X and for taking a less filtered approach to responses compared to some competitors. Available to X Premium subscribers and via the Grok standalone app.
Generative AI
AI that can create new content — text, images, audio, video, code — rather than just analysing or classifying existing content. ChatGPT generating a business proposal, DALL-E creating an image from a description, and GitHub Copilot writing code are all examples of generative AI in action.
GPT (Generative Pre-trained Transformer)
The type of AI model that powers ChatGPT, developed by OpenAI. GPT models are trained on vast amounts of text data and can generate human-like text, answer questions, summarize documents, write code, and much more. The 'transformer' part refers to the underlying architecture that makes modern AI so powerful.
Guardrails
Rules, filters, and safety mechanisms built into AI systems to prevent them from producing harmful, offensive, or inappropriate outputs. Guardrails are why AI assistants refuse certain requests. When building custom AI tools for businesses, we also add business-specific guardrails — for example, preventing a customer service bot from making promises the business can't keep.
Hallucination
When an AI confidently produces information that is factually incorrect or completely made up. This is one of the most important limitations of current AI tools to understand. AI doesn't 'know' things the way humans do — it predicts likely text, which can sometimes result in plausible-sounding but wrong answers. Always verify important facts.
Human-in-the-Loop
A system design where humans are involved in reviewing, approving, or correcting AI outputs before they're acted on. For most business applications, keeping a human in the loop is best practice — especially for anything customer-facing, financial, or legally sensitive.
Hyperparameter
Settings that control how an AI model is trained — like how fast it learns, how many training rounds it goes through, or how large its layers are. Unlike regular parameters (which the model learns automatically), hyperparameters are set by the people building the model. Getting them right is part of the craft of AI development.
Inference
The process of using a trained AI model to generate outputs from new inputs. When you type a question into ChatGPT and it responds, that's inference — the model is applying what it learned during training to your specific input. Training happens once; inference happens every time you use the tool.
Integration
Connecting two or more software systems so they can share data and work together. In AI, integration typically means connecting an AI tool to your existing business software — like linking a chatbot to your CRM, or connecting an automation tool to your booking system.
Jailbreak
An attempt to bypass the safety guidelines or restrictions built into an AI model — usually by crafting clever prompts that trick the AI into ignoring its rules. AI companies work continuously to prevent jailbreaks. As a business owner, it's worth knowing this exists so you understand why AI tools sometimes refuse certain requests — those guardrails are there for good reason.
Knowledge Base
A structured collection of information that an AI can draw on to answer questions. When we build a custom AI assistant for your business, we often create a knowledge base containing your FAQs, product details, policies, and procedures — so the AI gives accurate, business-specific answers rather than generic ones.
Large Language Model (LLM)
An AI model trained on enormous amounts of text data that can understand and generate human language. ChatGPT, Claude, and Gemini are all LLMs. They're called 'large' because of the sheer scale of data they're trained on and the number of parameters (internal settings) they contain — often in the billions.
Llama
Meta's family of open-source large language models, released for anyone to download, run, and build on. Unlike ChatGPT or Claude, Llama models can be run locally on your own hardware without sending data to a third-party server — making them popular for privacy-sensitive applications and businesses that want more control over their AI stack.
Machine Learning (ML)
A branch of AI where systems learn from data and improve their performance over time without being explicitly programmed for every scenario. Instead of following rigid rules, machine learning models identify patterns in data and use those patterns to make predictions or decisions.
Multimodal AI
AI that can process and generate multiple types of content — text, images, audio, and video — rather than just one. GPT-4o is multimodal: you can show it a photo and ask a question about it, or have it describe an image. Multimodal AI opens up a much wider range of business applications than text-only models.
Model
In AI, a model is the trained system that takes inputs and produces outputs. When people refer to 'the model', they mean the AI itself — the mathematical structure that has been trained on data. Different models have different strengths, weaknesses, and use cases.
Natural Language Processing (NLP)
The branch of AI focused on enabling computers to understand, interpret, and generate human language. NLP powers chatbots, voice assistants, translation tools, sentiment analysis, and much more. When an AI understands what you're asking in plain English, that's NLP at work.
Neural Network
A type of AI system loosely inspired by the structure of the human brain, made up of layers of interconnected nodes that process information. Neural networks are the foundation of most modern AI — including image recognition, speech recognition, and large language models.
No-Code / Low-Code
Tools and platforms that allow people to build software applications and automations without writing traditional code. Many AI tools are now available in no-code or low-code formats, making them accessible to business owners without technical backgrounds. Zapier and Make are popular examples.
OpenAI
The company behind ChatGPT, GPT-4, and DALL-E. OpenAI is one of the most influential AI research organizations in the world. Founded in 2015, it has become the most recognized name in consumer AI and provides the underlying technology for many third-party AI tools and applications.
Overfitting
When an AI model learns its training data too well — including the noise and quirks — and as a result performs poorly on new, real-world data. Think of it like a student who memorises past exam papers word-for-word but can't answer a question that's phrased slightly differently. Overfitting is a common challenge in building reliable AI systems.
Parameters
The internal settings of an AI model that are adjusted during training to improve performance. A model with more parameters is generally more capable but also more expensive to run. GPT-4 is estimated to have hundreds of billions of parameters — which is part of why it's so capable.
Prompt
The input you give to an AI model — the question, instruction, or context that tells the AI what you want it to do. Writing effective prompts (prompt engineering) is a skill that significantly affects the quality of AI outputs. A well-crafted prompt can be the difference between a mediocre and an excellent result.
Prompt Engineering
The practice of designing and refining prompts to get better outputs from AI models. It involves understanding how AI models interpret instructions and structuring your inputs to guide the model toward the response you want. Good prompt engineering is one of the most practical AI skills a business owner can develop.
Prompt Injection
A type of attack where a malicious user crafts inputs designed to override an AI system's instructions and make it behave in unintended ways. For example, a user might try to trick a customer service chatbot into ignoring its rules by embedding hidden instructions in their message. It's a real security concern for businesses deploying AI-facing tools — and something we account for when building AI systems for clients.
Query
A question or request submitted to an AI system, search engine, or database. In everyday AI use, a query is essentially what you type into ChatGPT or a search bar. In more technical contexts, queries are used to retrieve specific data from databases. The quality of your query directly affects the quality of the answer you get back.
RAG (Retrieval-Augmented Generation)
A technique that combines an AI language model with a search system, allowing the AI to retrieve relevant information from a specific knowledge base before generating a response. RAG is how you build AI tools that can answer questions about your specific business — your products, policies, FAQs — rather than just general knowledge.
Sentiment Analysis
An AI technique that identifies and categorises the emotional tone of text — positive, negative, or neutral. Businesses use sentiment analysis to monitor customer reviews, social media mentions, and support tickets to understand how customers feel about their products or services at scale.
Shadow AI
The use of AI tools by employees without the knowledge or approval of their organization's IT or management team. Just as 'shadow IT' described unauthorised software use, shadow AI is a growing concern — staff may be pasting sensitive client data into ChatGPT or using unapproved AI tools, creating data privacy and security risks. Having a clear AI policy in your business is increasingly important.
System Prompt
A set of instructions given to an AI model before a conversation begins, used to define its persona, rules, and behavior. When we build a custom AI chatbot for a business, the system prompt is where we tell it who it is, what it can and can't do, and how it should respond to customers.
Token
The basic unit of text that AI language models process. A token is roughly equivalent to a word or part of a word — 'hamburger' might be split into two tokens, while 'the' is one. AI models have token limits for inputs and outputs, and usage is often billed per token. Understanding tokens helps you use AI tools more efficiently.
Training
The process of teaching an AI model by exposing it to large amounts of data and adjusting its parameters to improve performance. Training a large AI model requires enormous computing power and can take weeks or months. Most businesses use pre-trained models rather than training their own from scratch.
Transformer
The underlying architecture that powers most modern AI language models, including GPT, Claude, and Gemini. Introduced by Google in 2017, the transformer architecture revolutionised AI by enabling models to process and understand context across long sequences of text far more effectively than previous approaches.
Use Case
A specific, practical application of a technology to solve a real problem. In AI, a use case might be 'using AI to automatically respond to common customer enquiries' or 'using AI to summarize meeting notes'. Identifying the right use cases for your business is the first step in any successful AI implementation.
Vector Database
A type of database designed to store and search data in the format that AI models use (vectors/embeddings). Vector databases are a key component of RAG systems — they allow AI tools to quickly find the most relevant information from a large knowledge base when answering a question.
Vibe Coding
A term coined in 2025 describing the practice of building software by describing what you want in plain English to an AI — and letting the AI write the code for you. Instead of learning to code, you 'vibe' with the AI: you describe the idea, review what it builds, give feedback, and iterate. Tools like Cursor, GitHub Copilot, and platforms like the one this website was built on make vibe coding accessible to non-developers. It's one of the most significant shifts in how software gets made.
Voice AI
AI systems that can understand and generate spoken language — enabling voice-based interactions with software. Voice AI powers smart speakers (Alexa, Siri, Google Assistant), AI phone receptionists, and voice-enabled customer service tools. For small businesses, voice AI can handle inbound calls, answer common questions, and route enquiries — 24 hours a day.
Workflow
A sequence of steps or tasks that make up a business process. In the context of AI and automation, a workflow is the series of actions that get triggered and executed automatically — for example, a new lead fills in a form, which triggers an AI to qualify them, send a welcome email, and add them to your CRM.
xAI
The AI company founded by Elon Musk in 2023, responsible for building Grok. xAI's stated mission is to understand the true nature of the universe through AI research. It's one of the newer major players in the AI space and is notable for its integration with the X (formerly Twitter) platform.
Yield (AI Output Quality)
In AI contexts, yield refers to the quality and usefulness of the outputs an AI produces relative to the inputs given. High-yield prompting means getting consistently valuable, accurate, and relevant responses. Improving your yield is largely about better prompts, clearer context, and choosing the right model for the task.
Zero-Shot Learning
The ability of an AI model to perform a task it hasn't been specifically trained on, using only a description or instruction. For example, asking ChatGPT to classify customer feedback into categories it's never seen before. Zero-shot capability is one of the things that makes modern LLMs so versatile for business use.
AI Washing
When a company exaggerates or falsely claims that its product uses AI to appear more innovative or attract investment. Similar to 'greenwashing' in sustainability. As AI becomes a buzzword, AI washing is increasingly common — it's worth asking vendors exactly how AI is used in their product, not just whether it is.
Algorithm
A set of step-by-step instructions a computer follows to complete a task or solve a problem. Think of it like a recipe — the computer follows each step in order to produce a result. Most AI tools are built on complex algorithms that process data and generate outputs.
Artificial Intelligence (AI)
Technology that enables computers to perform tasks that normally require human intelligence — things like understanding language, recognizing images, making decisions, and learning from experience. AI is an umbrella term that covers many different technologies and approaches.
Automation
Using technology to perform tasks with little or no human input. In a business context, automation means setting up systems that handle repetitive work automatically — like sending follow-up emails, updating spreadsheets, or routing customer enquiries — so your team can focus on higher-value work.
Agentic AI
AI that can take actions and make decisions autonomously to complete a goal — not just answer questions, but actually do things like browse the web, send emails, or interact with software on your behalf. Agentic AI is one of the fastest-growing areas of AI development.
AI-Native
A product, business, or workflow that is built from the ground up with AI at its core — rather than having AI bolted on as an afterthought. An AI-native business uses AI as a fundamental part of how it operates, not just as an occasional tool. Increasingly, being AI-native is a competitive advantage.
API (Application Programming Interface)
A way for two software applications to talk to each other. When we integrate an AI tool with your existing software, we're usually using APIs to connect them. You don't need to understand how APIs work — just know they're the plumbing that makes integrations possible.
ChatGPT
The AI assistant built by OpenAI and one of the most widely used AI tools in the world. ChatGPT can write, summarize, answer questions, draft emails, generate ideas, and much more — all through a simple chat interface. It runs on OpenAI's GPT models and is available free or via a paid plan. For most small businesses, it's the best starting point for exploring what AI can do.
Claude
An AI assistant built by Anthropic, widely regarded as one of the best alternatives to ChatGPT. Claude is known for nuanced writing, long-document analysis, and following complex instructions carefully. It's designed with a strong focus on safety and honesty. Available at claude.ai with a free tier and paid plans.
Bot
Short for 'robot' — a software program that performs automated tasks. In business, bots are commonly used for customer service (chatbots), data collection, and repetitive admin tasks. Not all bots use AI, but AI-powered bots are far more capable than simple rule-based ones.
Benchmark
A standardised test used to measure and compare the performance of AI models. When you see claims like 'our AI scores 90% on X benchmark', it means the model was tested against a standard set of problems. Benchmarks help compare different AI tools, though real-world performance can vary.
Black Box
A system where you can see the inputs and outputs but not what happens in between. Most AI models are black boxes — you give them a prompt and get a response, but the internal reasoning is not visible or easily explainable. This is why 'explainable AI' is an active area of research, particularly in regulated industries like finance and healthcare.
Chatbot
A software program designed to simulate conversation with humans, typically via text. Modern AI-powered chatbots (like those built on ChatGPT) can understand natural language, answer complex questions, and handle customer service tasks around the clock — far beyond the simple 'press 1 for billing' bots of the past.
Context Window
The amount of text an AI model can 'see' and consider at one time. Think of it like short-term memory — the larger the context window, the more of a conversation or document the AI can take into account when generating a response. Larger context windows generally produce more coherent, relevant outputs.
Copilot
An AI assistant that works alongside a human to help them do their job better — not replacing them, but augmenting their capabilities. Microsoft's Copilot (built into Office 365) is a well-known example. The term reflects the idea that the human is still in control; the AI is just helping navigate.
Data
Information in a form that computers can process. AI systems learn from data — the more relevant, high-quality data an AI is trained on, the better it tends to perform. In a business context, your data includes things like customer records, transaction history, emails, and documents.
Deepfake
AI-generated video, audio, or images that realistically depict someone saying or doing something they never actually said or did. Deepfakes are created using deep learning techniques and have become increasingly convincing. As a business owner, it's important to be aware of deepfakes — both as a potential security risk (e.g. fake audio of a CEO authorising a transfer) and as a reputational concern.
Deep Learning
A type of machine learning that uses neural networks with many layers to learn from large amounts of data. Deep learning powers many of the most impressive AI capabilities today — including image recognition, speech recognition, and large language models like ChatGPT.
Embedding
A way of representing text, images, or other data as numbers so that an AI can understand relationships between concepts. Embeddings allow AI to understand that 'dog' and 'puppy' are related, or that 'invoice' and 'bill' mean similar things — even without being explicitly told.
Emergent Behavior
Unexpected capabilities that appear in AI models as they grow larger or are trained on more data — abilities that weren't explicitly programmed or anticipated. For example, large language models spontaneously developed the ability to do basic arithmetic and translate languages without being specifically trained to do so. Emergent behavior is one of the reasons AI development can be unpredictable.
Fine-Tuning
The process of taking a pre-trained AI model and training it further on a specific dataset to make it better at a particular task. For example, fine-tuning a general language model on your company's documents and tone of voice so it writes in your style and understands your business context.
Frontier Model
The most advanced AI models available at any given time — the cutting edge of what's technically possible. GPT-4o, Claude 3.5 Sonnet, and Gemini Ultra are examples of frontier models. The term is used in AI policy and safety discussions to describe models powerful enough to potentially pose novel risks alongside their capabilities.
Foundation Model
A large AI model trained on a broad dataset that can be adapted for many different tasks. GPT-4, Claude, and Gemini are all foundation models. They're called 'foundation' models because they serve as the base that other, more specialized AI applications are built on top of.
Gemini
Google's flagship AI assistant and language model, available at gemini.google.com. Gemini integrates natively with Google Workspace — Gmail, Docs, Sheets, Drive — making it a strong choice for businesses already running on Google tools. It competes directly with ChatGPT and Claude and is available with a free tier and paid Google One AI Premium plan.
Grok
An AI assistant built by xAI, Elon Musk's AI company, and integrated into the X (formerly Twitter) platform. Grok is notable for having real-time access to information posted on X and for taking a less filtered approach to responses compared to some competitors. Available to X Premium subscribers and via the Grok standalone app.
Generative AI
AI that can create new content — text, images, audio, video, code — rather than just analysing or classifying existing content. ChatGPT generating a business proposal, DALL-E creating an image from a description, and GitHub Copilot writing code are all examples of generative AI in action.
GPT (Generative Pre-trained Transformer)
The type of AI model that powers ChatGPT, developed by OpenAI. GPT models are trained on vast amounts of text data and can generate human-like text, answer questions, summarize documents, write code, and much more. The 'transformer' part refers to the underlying architecture that makes modern AI so powerful.
Guardrails
Rules, filters, and safety mechanisms built into AI systems to prevent them from producing harmful, offensive, or inappropriate outputs. Guardrails are why AI assistants refuse certain requests. When building custom AI tools for businesses, we also add business-specific guardrails — for example, preventing a customer service bot from making promises the business can't keep.
Hallucination
When an AI confidently produces information that is factually incorrect or completely made up. This is one of the most important limitations of current AI tools to understand. AI doesn't 'know' things the way humans do — it predicts likely text, which can sometimes result in plausible-sounding but wrong answers. Always verify important facts.
Human-in-the-Loop
A system design where humans are involved in reviewing, approving, or correcting AI outputs before they're acted on. For most business applications, keeping a human in the loop is best practice — especially for anything customer-facing, financial, or legally sensitive.
Hyperparameter
Settings that control how an AI model is trained — like how fast it learns, how many training rounds it goes through, or how large its layers are. Unlike regular parameters (which the model learns automatically), hyperparameters are set by the people building the model. Getting them right is part of the craft of AI development.
Inference
The process of using a trained AI model to generate outputs from new inputs. When you type a question into ChatGPT and it responds, that's inference — the model is applying what it learned during training to your specific input. Training happens once; inference happens every time you use the tool.
Integration
Connecting two or more software systems so they can share data and work together. In AI, integration typically means connecting an AI tool to your existing business software — like linking a chatbot to your CRM, or connecting an automation tool to your booking system.
Jailbreak
An attempt to bypass the safety guidelines or restrictions built into an AI model — usually by crafting clever prompts that trick the AI into ignoring its rules. AI companies work continuously to prevent jailbreaks. As a business owner, it's worth knowing this exists so you understand why AI tools sometimes refuse certain requests — those guardrails are there for good reason.
Knowledge Base
A structured collection of information that an AI can draw on to answer questions. When we build a custom AI assistant for your business, we often create a knowledge base containing your FAQs, product details, policies, and procedures — so the AI gives accurate, business-specific answers rather than generic ones.
Large Language Model (LLM)
An AI model trained on enormous amounts of text data that can understand and generate human language. ChatGPT, Claude, and Gemini are all LLMs. They're called 'large' because of the sheer scale of data they're trained on and the number of parameters (internal settings) they contain — often in the billions.
Llama
Meta's family of open-source large language models, released for anyone to download, run, and build on. Unlike ChatGPT or Claude, Llama models can be run locally on your own hardware without sending data to a third-party server — making them popular for privacy-sensitive applications and businesses that want more control over their AI stack.
Machine Learning (ML)
A branch of AI where systems learn from data and improve their performance over time without being explicitly programmed for every scenario. Instead of following rigid rules, machine learning models identify patterns in data and use those patterns to make predictions or decisions.
Multimodal AI
AI that can process and generate multiple types of content — text, images, audio, and video — rather than just one. GPT-4o is multimodal: you can show it a photo and ask a question about it, or have it describe an image. Multimodal AI opens up a much wider range of business applications than text-only models.
Model
In AI, a model is the trained system that takes inputs and produces outputs. When people refer to 'the model', they mean the AI itself — the mathematical structure that has been trained on data. Different models have different strengths, weaknesses, and use cases.
Natural Language Processing (NLP)
The branch of AI focused on enabling computers to understand, interpret, and generate human language. NLP powers chatbots, voice assistants, translation tools, sentiment analysis, and much more. When an AI understands what you're asking in plain English, that's NLP at work.
Neural Network
A type of AI system loosely inspired by the structure of the human brain, made up of layers of interconnected nodes that process information. Neural networks are the foundation of most modern AI — including image recognition, speech recognition, and large language models.
No-Code / Low-Code
Tools and platforms that allow people to build software applications and automations without writing traditional code. Many AI tools are now available in no-code or low-code formats, making them accessible to business owners without technical backgrounds. Zapier and Make are popular examples.
OpenAI
The company behind ChatGPT, GPT-4, and DALL-E. OpenAI is one of the most influential AI research organizations in the world. Founded in 2015, it has become the most recognized name in consumer AI and provides the underlying technology for many third-party AI tools and applications.
Overfitting
When an AI model learns its training data too well — including the noise and quirks — and as a result performs poorly on new, real-world data. Think of it like a student who memorises past exam papers word-for-word but can't answer a question that's phrased slightly differently. Overfitting is a common challenge in building reliable AI systems.
Parameters
The internal settings of an AI model that are adjusted during training to improve performance. A model with more parameters is generally more capable but also more expensive to run. GPT-4 is estimated to have hundreds of billions of parameters — which is part of why it's so capable.
Prompt
The input you give to an AI model — the question, instruction, or context that tells the AI what you want it to do. Writing effective prompts (prompt engineering) is a skill that significantly affects the quality of AI outputs. A well-crafted prompt can be the difference between a mediocre and an excellent result.
Prompt Engineering
The practice of designing and refining prompts to get better outputs from AI models. It involves understanding how AI models interpret instructions and structuring your inputs to guide the model toward the response you want. Good prompt engineering is one of the most practical AI skills a business owner can develop.
Prompt Injection
A type of attack where a malicious user crafts inputs designed to override an AI system's instructions and make it behave in unintended ways. For example, a user might try to trick a customer service chatbot into ignoring its rules by embedding hidden instructions in their message. It's a real security concern for businesses deploying AI-facing tools — and something we account for when building AI systems for clients.
Query
A question or request submitted to an AI system, search engine, or database. In everyday AI use, a query is essentially what you type into ChatGPT or a search bar. In more technical contexts, queries are used to retrieve specific data from databases. The quality of your query directly affects the quality of the answer you get back.
RAG (Retrieval-Augmented Generation)
A technique that combines an AI language model with a search system, allowing the AI to retrieve relevant information from a specific knowledge base before generating a response. RAG is how you build AI tools that can answer questions about your specific business — your products, policies, FAQs — rather than just general knowledge.
Sentiment Analysis
An AI technique that identifies and categorises the emotional tone of text — positive, negative, or neutral. Businesses use sentiment analysis to monitor customer reviews, social media mentions, and support tickets to understand how customers feel about their products or services at scale.
Shadow AI
The use of AI tools by employees without the knowledge or approval of their organization's IT or management team. Just as 'shadow IT' described unauthorised software use, shadow AI is a growing concern — staff may be pasting sensitive client data into ChatGPT or using unapproved AI tools, creating data privacy and security risks. Having a clear AI policy in your business is increasingly important.
System Prompt
A set of instructions given to an AI model before a conversation begins, used to define its persona, rules, and behavior. When we build a custom AI chatbot for a business, the system prompt is where we tell it who it is, what it can and can't do, and how it should respond to customers.
Token
The basic unit of text that AI language models process. A token is roughly equivalent to a word or part of a word — 'hamburger' might be split into two tokens, while 'the' is one. AI models have token limits for inputs and outputs, and usage is often billed per token. Understanding tokens helps you use AI tools more efficiently.
Training
The process of teaching an AI model by exposing it to large amounts of data and adjusting its parameters to improve performance. Training a large AI model requires enormous computing power and can take weeks or months. Most businesses use pre-trained models rather than training their own from scratch.
Transformer
The underlying architecture that powers most modern AI language models, including GPT, Claude, and Gemini. Introduced by Google in 2017, the transformer architecture revolutionised AI by enabling models to process and understand context across long sequences of text far more effectively than previous approaches.
Use Case
A specific, practical application of a technology to solve a real problem. In AI, a use case might be 'using AI to automatically respond to common customer enquiries' or 'using AI to summarize meeting notes'. Identifying the right use cases for your business is the first step in any successful AI implementation.
Vector Database
A type of database designed to store and search data in the format that AI models use (vectors/embeddings). Vector databases are a key component of RAG systems — they allow AI tools to quickly find the most relevant information from a large knowledge base when answering a question.
Vibe Coding
A term coined in 2025 describing the practice of building software by describing what you want in plain English to an AI — and letting the AI write the code for you. Instead of learning to code, you 'vibe' with the AI: you describe the idea, review what it builds, give feedback, and iterate. Tools like Cursor, GitHub Copilot, and platforms like the one this website was built on make vibe coding accessible to non-developers. It's one of the most significant shifts in how software gets made.
Voice AI
AI systems that can understand and generate spoken language — enabling voice-based interactions with software. Voice AI powers smart speakers (Alexa, Siri, Google Assistant), AI phone receptionists, and voice-enabled customer service tools. For small businesses, voice AI can handle inbound calls, answer common questions, and route enquiries — 24 hours a day.
Workflow
A sequence of steps or tasks that make up a business process. In the context of AI and automation, a workflow is the series of actions that get triggered and executed automatically — for example, a new lead fills in a form, which triggers an AI to qualify them, send a welcome email, and add them to your CRM.
xAI
The AI company founded by Elon Musk in 2023, responsible for building Grok. xAI's stated mission is to understand the true nature of the universe through AI research. It's one of the newer major players in the AI space and is notable for its integration with the X (formerly Twitter) platform.
Yield (AI Output Quality)
In AI contexts, yield refers to the quality and usefulness of the outputs an AI produces relative to the inputs given. High-yield prompting means getting consistently valuable, accurate, and relevant responses. Improving your yield is largely about better prompts, clearer context, and choosing the right model for the task.
Zero-Shot Learning
The ability of an AI model to perform a task it hasn't been specifically trained on, using only a description or instruction. For example, asking ChatGPT to classify customer feedback into categories it's never seen before. Zero-shot capability is one of the things that makes modern LLMs so versatile for business use.
AI Washing
When a company exaggerates or falsely claims that its product uses AI to appear more innovative or attract investment. Similar to 'greenwashing' in sustainability. As AI becomes a buzzword, AI washing is increasingly common — it's worth asking vendors exactly how AI is used in their product, not just whether it is.
Algorithm
A set of step-by-step instructions a computer follows to complete a task or solve a problem. Think of it like a recipe — the computer follows each step in order to produce a result. Most AI tools are built on complex algorithms that process data and generate outputs.
Artificial Intelligence (AI)
Technology that enables computers to perform tasks that normally require human intelligence — things like understanding language, recognizing images, making decisions, and learning from experience. AI is an umbrella term that covers many different technologies and approaches.
Automation
Using technology to perform tasks with little or no human input. In a business context, automation means setting up systems that handle repetitive work automatically — like sending follow-up emails, updating spreadsheets, or routing customer enquiries — so your team can focus on higher-value work.
Agentic AI
AI that can take actions and make decisions autonomously to complete a goal — not just answer questions, but actually do things like browse the web, send emails, or interact with software on your behalf. Agentic AI is one of the fastest-growing areas of AI development.
AI-Native
A product, business, or workflow that is built from the ground up with AI at its core — rather than having AI bolted on as an afterthought. An AI-native business uses AI as a fundamental part of how it operates, not just as an occasional tool. Increasingly, being AI-native is a competitive advantage.
API (Application Programming Interface)
A way for two software applications to talk to each other. When we integrate an AI tool with your existing software, we're usually using APIs to connect them. You don't need to understand how APIs work — just know they're the plumbing that makes integrations possible.
ChatGPT
The AI assistant built by OpenAI and one of the most widely used AI tools in the world. ChatGPT can write, summarize, answer questions, draft emails, generate ideas, and much more — all through a simple chat interface. It runs on OpenAI's GPT models and is available free or via a paid plan. For most small businesses, it's the best starting point for exploring what AI can do.
Claude
An AI assistant built by Anthropic, widely regarded as one of the best alternatives to ChatGPT. Claude is known for nuanced writing, long-document analysis, and following complex instructions carefully. It's designed with a strong focus on safety and honesty. Available at claude.ai with a free tier and paid plans.
Bot
Short for 'robot' — a software program that performs automated tasks. In business, bots are commonly used for customer service (chatbots), data collection, and repetitive admin tasks. Not all bots use AI, but AI-powered bots are far more capable than simple rule-based ones.
Benchmark
A standardised test used to measure and compare the performance of AI models. When you see claims like 'our AI scores 90% on X benchmark', it means the model was tested against a standard set of problems. Benchmarks help compare different AI tools, though real-world performance can vary.
Black Box
A system where you can see the inputs and outputs but not what happens in between. Most AI models are black boxes — you give them a prompt and get a response, but the internal reasoning is not visible or easily explainable. This is why 'explainable AI' is an active area of research, particularly in regulated industries like finance and healthcare.
Chatbot
A software program designed to simulate conversation with humans, typically via text. Modern AI-powered chatbots (like those built on ChatGPT) can understand natural language, answer complex questions, and handle customer service tasks around the clock — far beyond the simple 'press 1 for billing' bots of the past.
Context Window
The amount of text an AI model can 'see' and consider at one time. Think of it like short-term memory — the larger the context window, the more of a conversation or document the AI can take into account when generating a response. Larger context windows generally produce more coherent, relevant outputs.
Copilot
An AI assistant that works alongside a human to help them do their job better — not replacing them, but augmenting their capabilities. Microsoft's Copilot (built into Office 365) is a well-known example. The term reflects the idea that the human is still in control; the AI is just helping navigate.
Data
Information in a form that computers can process. AI systems learn from data — the more relevant, high-quality data an AI is trained on, the better it tends to perform. In a business context, your data includes things like customer records, transaction history, emails, and documents.
Deepfake
AI-generated video, audio, or images that realistically depict someone saying or doing something they never actually said or did. Deepfakes are created using deep learning techniques and have become increasingly convincing. As a business owner, it's important to be aware of deepfakes — both as a potential security risk (e.g. fake audio of a CEO authorising a transfer) and as a reputational concern.
Deep Learning
A type of machine learning that uses neural networks with many layers to learn from large amounts of data. Deep learning powers many of the most impressive AI capabilities today — including image recognition, speech recognition, and large language models like ChatGPT.
Embedding
A way of representing text, images, or other data as numbers so that an AI can understand relationships between concepts. Embeddings allow AI to understand that 'dog' and 'puppy' are related, or that 'invoice' and 'bill' mean similar things — even without being explicitly told.
Emergent Behavior
Unexpected capabilities that appear in AI models as they grow larger or are trained on more data — abilities that weren't explicitly programmed or anticipated. For example, large language models spontaneously developed the ability to do basic arithmetic and translate languages without being specifically trained to do so. Emergent behavior is one of the reasons AI development can be unpredictable.
Fine-Tuning
The process of taking a pre-trained AI model and training it further on a specific dataset to make it better at a particular task. For example, fine-tuning a general language model on your company's documents and tone of voice so it writes in your style and understands your business context.
Frontier Model
The most advanced AI models available at any given time — the cutting edge of what's technically possible. GPT-4o, Claude 3.5 Sonnet, and Gemini Ultra are examples of frontier models. The term is used in AI policy and safety discussions to describe models powerful enough to potentially pose novel risks alongside their capabilities.
Foundation Model
A large AI model trained on a broad dataset that can be adapted for many different tasks. GPT-4, Claude, and Gemini are all foundation models. They're called 'foundation' models because they serve as the base that other, more specialized AI applications are built on top of.
Gemini
Google's flagship AI assistant and language model, available at gemini.google.com. Gemini integrates natively with Google Workspace — Gmail, Docs, Sheets, Drive — making it a strong choice for businesses already running on Google tools. It competes directly with ChatGPT and Claude and is available with a free tier and paid Google One AI Premium plan.
Grok
An AI assistant built by xAI, Elon Musk's AI company, and integrated into the X (formerly Twitter) platform. Grok is notable for having real-time access to information posted on X and for taking a less filtered approach to responses compared to some competitors. Available to X Premium subscribers and via the Grok standalone app.
Generative AI
AI that can create new content — text, images, audio, video, code — rather than just analysing or classifying existing content. ChatGPT generating a business proposal, DALL-E creating an image from a description, and GitHub Copilot writing code are all examples of generative AI in action.
GPT (Generative Pre-trained Transformer)
The type of AI model that powers ChatGPT, developed by OpenAI. GPT models are trained on vast amounts of text data and can generate human-like text, answer questions, summarize documents, write code, and much more. The 'transformer' part refers to the underlying architecture that makes modern AI so powerful.
Guardrails
Rules, filters, and safety mechanisms built into AI systems to prevent them from producing harmful, offensive, or inappropriate outputs. Guardrails are why AI assistants refuse certain requests. When building custom AI tools for businesses, we also add business-specific guardrails — for example, preventing a customer service bot from making promises the business can't keep.
Hallucination
When an AI confidently produces information that is factually incorrect or completely made up. This is one of the most important limitations of current AI tools to understand. AI doesn't 'know' things the way humans do — it predicts likely text, which can sometimes result in plausible-sounding but wrong answers. Always verify important facts.
Human-in-the-Loop
A system design where humans are involved in reviewing, approving, or correcting AI outputs before they're acted on. For most business applications, keeping a human in the loop is best practice — especially for anything customer-facing, financial, or legally sensitive.
Hyperparameter
Settings that control how an AI model is trained — like how fast it learns, how many training rounds it goes through, or how large its layers are. Unlike regular parameters (which the model learns automatically), hyperparameters are set by the people building the model. Getting them right is part of the craft of AI development.
Inference
The process of using a trained AI model to generate outputs from new inputs. When you type a question into ChatGPT and it responds, that's inference — the model is applying what it learned during training to your specific input. Training happens once; inference happens every time you use the tool.
Integration
Connecting two or more software systems so they can share data and work together. In AI, integration typically means connecting an AI tool to your existing business software — like linking a chatbot to your CRM, or connecting an automation tool to your booking system.
Jailbreak
An attempt to bypass the safety guidelines or restrictions built into an AI model — usually by crafting clever prompts that trick the AI into ignoring its rules. AI companies work continuously to prevent jailbreaks. As a business owner, it's worth knowing this exists so you understand why AI tools sometimes refuse certain requests — those guardrails are there for good reason.
Knowledge Base
A structured collection of information that an AI can draw on to answer questions. When we build a custom AI assistant for your business, we often create a knowledge base containing your FAQs, product details, policies, and procedures — so the AI gives accurate, business-specific answers rather than generic ones.
Large Language Model (LLM)
An AI model trained on enormous amounts of text data that can understand and generate human language. ChatGPT, Claude, and Gemini are all LLMs. They're called 'large' because of the sheer scale of data they're trained on and the number of parameters (internal settings) they contain — often in the billions.
Llama
Meta's family of open-source large language models, released for anyone to download, run, and build on. Unlike ChatGPT or Claude, Llama models can be run locally on your own hardware without sending data to a third-party server — making them popular for privacy-sensitive applications and businesses that want more control over their AI stack.
Machine Learning (ML)
A branch of AI where systems learn from data and improve their performance over time without being explicitly programmed for every scenario. Instead of following rigid rules, machine learning models identify patterns in data and use those patterns to make predictions or decisions.
Multimodal AI
AI that can process and generate multiple types of content — text, images, audio, and video — rather than just one. GPT-4o is multimodal: you can show it a photo and ask a question about it, or have it describe an image. Multimodal AI opens up a much wider range of business applications than text-only models.
Model
In AI, a model is the trained system that takes inputs and produces outputs. When people refer to 'the model', they mean the AI itself — the mathematical structure that has been trained on data. Different models have different strengths, weaknesses, and use cases.
Natural Language Processing (NLP)
The branch of AI focused on enabling computers to understand, interpret, and generate human language. NLP powers chatbots, voice assistants, translation tools, sentiment analysis, and much more. When an AI understands what you're asking in plain English, that's NLP at work.
Neural Network
A type of AI system loosely inspired by the structure of the human brain, made up of layers of interconnected nodes that process information. Neural networks are the foundation of most modern AI — including image recognition, speech recognition, and large language models.
No-Code / Low-Code
Tools and platforms that allow people to build software applications and automations without writing traditional code. Many AI tools are now available in no-code or low-code formats, making them accessible to business owners without technical backgrounds. Zapier and Make are popular examples.
OpenAI
The company behind ChatGPT, GPT-4, and DALL-E. OpenAI is one of the most influential AI research organizations in the world. Founded in 2015, it has become the most recognized name in consumer AI and provides the underlying technology for many third-party AI tools and applications.
Overfitting
When an AI model learns its training data too well — including the noise and quirks — and as a result performs poorly on new, real-world data. Think of it like a student who memorises past exam papers word-for-word but can't answer a question that's phrased slightly differently. Overfitting is a common challenge in building reliable AI systems.
Parameters
The internal settings of an AI model that are adjusted during training to improve performance. A model with more parameters is generally more capable but also more expensive to run. GPT-4 is estimated to have hundreds of billions of parameters — which is part of why it's so capable.
Prompt
The input you give to an AI model — the question, instruction, or context that tells the AI what you want it to do. Writing effective prompts (prompt engineering) is a skill that significantly affects the quality of AI outputs. A well-crafted prompt can be the difference between a mediocre and an excellent result.
Prompt Engineering
The practice of designing and refining prompts to get better outputs from AI models. It involves understanding how AI models interpret instructions and structuring your inputs to guide the model toward the response you want. Good prompt engineering is one of the most practical AI skills a business owner can develop.
Prompt Injection
A type of attack where a malicious user crafts inputs designed to override an AI system's instructions and make it behave in unintended ways. For example, a user might try to trick a customer service chatbot into ignoring its rules by embedding hidden instructions in their message. It's a real security concern for businesses deploying AI-facing tools — and something we account for when building AI systems for clients.
Query
A question or request submitted to an AI system, search engine, or database. In everyday AI use, a query is essentially what you type into ChatGPT or a search bar. In more technical contexts, queries are used to retrieve specific data from databases. The quality of your query directly affects the quality of the answer you get back.
RAG (Retrieval-Augmented Generation)
A technique that combines an AI language model with a search system, allowing the AI to retrieve relevant information from a specific knowledge base before generating a response. RAG is how you build AI tools that can answer questions about your specific business — your products, policies, FAQs — rather than just general knowledge.
Sentiment Analysis
An AI technique that identifies and categorises the emotional tone of text — positive, negative, or neutral. Businesses use sentiment analysis to monitor customer reviews, social media mentions, and support tickets to understand how customers feel about their products or services at scale.
Shadow AI
The use of AI tools by employees without the knowledge or approval of their organization's IT or management team. Just as 'shadow IT' described unauthorised software use, shadow AI is a growing concern — staff may be pasting sensitive client data into ChatGPT or using unapproved AI tools, creating data privacy and security risks. Having a clear AI policy in your business is increasingly important.
System Prompt
A set of instructions given to an AI model before a conversation begins, used to define its persona, rules, and behavior. When we build a custom AI chatbot for a business, the system prompt is where we tell it who it is, what it can and can't do, and how it should respond to customers.
Token
The basic unit of text that AI language models process. A token is roughly equivalent to a word or part of a word — 'hamburger' might be split into two tokens, while 'the' is one. AI models have token limits for inputs and outputs, and usage is often billed per token. Understanding tokens helps you use AI tools more efficiently.
Training
The process of teaching an AI model by exposing it to large amounts of data and adjusting its parameters to improve performance. Training a large AI model requires enormous computing power and can take weeks or months. Most businesses use pre-trained models rather than training their own from scratch.
Transformer
The underlying architecture that powers most modern AI language models, including GPT, Claude, and Gemini. Introduced by Google in 2017, the transformer architecture revolutionised AI by enabling models to process and understand context across long sequences of text far more effectively than previous approaches.
Use Case
A specific, practical application of a technology to solve a real problem. In AI, a use case might be 'using AI to automatically respond to common customer enquiries' or 'using AI to summarize meeting notes'. Identifying the right use cases for your business is the first step in any successful AI implementation.
Vector Database
A type of database designed to store and search data in the format that AI models use (vectors/embeddings). Vector databases are a key component of RAG systems — they allow AI tools to quickly find the most relevant information from a large knowledge base when answering a question.
Vibe Coding
A term coined in 2025 describing the practice of building software by describing what you want in plain English to an AI — and letting the AI write the code for you. Instead of learning to code, you 'vibe' with the AI: you describe the idea, review what it builds, give feedback, and iterate. Tools like Cursor, GitHub Copilot, and platforms like the one this website was built on make vibe coding accessible to non-developers. It's one of the most significant shifts in how software gets made.
Voice AI
AI systems that can understand and generate spoken language — enabling voice-based interactions with software. Voice AI powers smart speakers (Alexa, Siri, Google Assistant), AI phone receptionists, and voice-enabled customer service tools. For small businesses, voice AI can handle inbound calls, answer common questions, and route enquiries — 24 hours a day.
Workflow
A sequence of steps or tasks that make up a business process. In the context of AI and automation, a workflow is the series of actions that get triggered and executed automatically — for example, a new lead fills in a form, which triggers an AI to qualify them, send a welcome email, and add them to your CRM.
xAI
The AI company founded by Elon Musk in 2023, responsible for building Grok. xAI's stated mission is to understand the true nature of the universe through AI research. It's one of the newer major players in the AI space and is notable for its integration with the X (formerly Twitter) platform.
Yield (AI Output Quality)
In AI contexts, yield refers to the quality and usefulness of the outputs an AI produces relative to the inputs given. High-yield prompting means getting consistently valuable, accurate, and relevant responses. Improving your yield is largely about better prompts, clearer context, and choosing the right model for the task.
Zero-Shot Learning
The ability of an AI model to perform a task it hasn't been specifically trained on, using only a description or instruction. For example, asking ChatGPT to classify customer feedback into categories it's never seen before. Zero-shot capability is one of the things that makes modern LLMs so versatile for business use.
AI Washing
When a company exaggerates or falsely claims that its product uses AI to appear more innovative or attract investment. Similar to 'greenwashing' in sustainability. As AI becomes a buzzword, AI washing is increasingly common — it's worth asking vendors exactly how AI is used in their product, not just whether it is.
Algorithm
A set of step-by-step instructions a computer follows to complete a task or solve a problem. Think of it like a recipe — the computer follows each step in order to produce a result. Most AI tools are built on complex algorithms that process data and generate outputs.
Artificial Intelligence (AI)
Technology that enables computers to perform tasks that normally require human intelligence — things like understanding language, recognizing images, making decisions, and learning from experience. AI is an umbrella term that covers many different technologies and approaches.
Automation
Using technology to perform tasks with little or no human input. In a business context, automation means setting up systems that handle repetitive work automatically — like sending follow-up emails, updating spreadsheets, or routing customer enquiries — so your team can focus on higher-value work.
Agentic AI
AI that can take actions and make decisions autonomously to complete a goal — not just answer questions, but actually do things like browse the web, send emails, or interact with software on your behalf. Agentic AI is one of the fastest-growing areas of AI development.
AI-Native
A product, business, or workflow that is built from the ground up with AI at its core — rather than having AI bolted on as an afterthought. An AI-native business uses AI as a fundamental part of how it operates, not just as an occasional tool. Increasingly, being AI-native is a competitive advantage.
API (Application Programming Interface)
A way for two software applications to talk to each other. When we integrate an AI tool with your existing software, we're usually using APIs to connect them. You don't need to understand how APIs work — just know they're the plumbing that makes integrations possible.
ChatGPT
The AI assistant built by OpenAI and one of the most widely used AI tools in the world. ChatGPT can write, summarize, answer questions, draft emails, generate ideas, and much more — all through a simple chat interface. It runs on OpenAI's GPT models and is available free or via a paid plan. For most small businesses, it's the best starting point for exploring what AI can do.
Claude
An AI assistant built by Anthropic, widely regarded as one of the best alternatives to ChatGPT. Claude is known for nuanced writing, long-document analysis, and following complex instructions carefully. It's designed with a strong focus on safety and honesty. Available at claude.ai with a free tier and paid plans.
Bot
Short for 'robot' — a software program that performs automated tasks. In business, bots are commonly used for customer service (chatbots), data collection, and repetitive admin tasks. Not all bots use AI, but AI-powered bots are far more capable than simple rule-based ones.
Benchmark
A standardised test used to measure and compare the performance of AI models. When you see claims like 'our AI scores 90% on X benchmark', it means the model was tested against a standard set of problems. Benchmarks help compare different AI tools, though real-world performance can vary.
Black Box
A system where you can see the inputs and outputs but not what happens in between. Most AI models are black boxes — you give them a prompt and get a response, but the internal reasoning is not visible or easily explainable. This is why 'explainable AI' is an active area of research, particularly in regulated industries like finance and healthcare.
Chatbot
A software program designed to simulate conversation with humans, typically via text. Modern AI-powered chatbots (like those built on ChatGPT) can understand natural language, answer complex questions, and handle customer service tasks around the clock — far beyond the simple 'press 1 for billing' bots of the past.
Context Window
The amount of text an AI model can 'see' and consider at one time. Think of it like short-term memory — the larger the context window, the more of a conversation or document the AI can take into account when generating a response. Larger context windows generally produce more coherent, relevant outputs.
Copilot
An AI assistant that works alongside a human to help them do their job better — not replacing them, but augmenting their capabilities. Microsoft's Copilot (built into Office 365) is a well-known example. The term reflects the idea that the human is still in control; the AI is just helping navigate.
Data
Information in a form that computers can process. AI systems learn from data — the more relevant, high-quality data an AI is trained on, the better it tends to perform. In a business context, your data includes things like customer records, transaction history, emails, and documents.
Deepfake
AI-generated video, audio, or images that realistically depict someone saying or doing something they never actually said or did. Deepfakes are created using deep learning techniques and have become increasingly convincing. As a business owner, it's important to be aware of deepfakes — both as a potential security risk (e.g. fake audio of a CEO authorising a transfer) and as a reputational concern.
Deep Learning
A type of machine learning that uses neural networks with many layers to learn from large amounts of data. Deep learning powers many of the most impressive AI capabilities today — including image recognition, speech recognition, and large language models like ChatGPT.
Embedding
A way of representing text, images, or other data as numbers so that an AI can understand relationships between concepts. Embeddings allow AI to understand that 'dog' and 'puppy' are related, or that 'invoice' and 'bill' mean similar things — even without being explicitly told.
Emergent Behavior
Unexpected capabilities that appear in AI models as they grow larger or are trained on more data — abilities that weren't explicitly programmed or anticipated. For example, large language models spontaneously developed the ability to do basic arithmetic and translate languages without being specifically trained to do so. Emergent behavior is one of the reasons AI development can be unpredictable.
Fine-Tuning
The process of taking a pre-trained AI model and training it further on a specific dataset to make it better at a particular task. For example, fine-tuning a general language model on your company's documents and tone of voice so it writes in your style and understands your business context.
Frontier Model
The most advanced AI models available at any given time — the cutting edge of what's technically possible. GPT-4o, Claude 3.5 Sonnet, and Gemini Ultra are examples of frontier models. The term is used in AI policy and safety discussions to describe models powerful enough to potentially pose novel risks alongside their capabilities.
Foundation Model
A large AI model trained on a broad dataset that can be adapted for many different tasks. GPT-4, Claude, and Gemini are all foundation models. They're called 'foundation' models because they serve as the base that other, more specialized AI applications are built on top of.
Gemini
Google's flagship AI assistant and language model, available at gemini.google.com. Gemini integrates natively with Google Workspace — Gmail, Docs, Sheets, Drive — making it a strong choice for businesses already running on Google tools. It competes directly with ChatGPT and Claude and is available with a free tier and paid Google One AI Premium plan.
Grok
An AI assistant built by xAI, Elon Musk's AI company, and integrated into the X (formerly Twitter) platform. Grok is notable for having real-time access to information posted on X and for taking a less filtered approach to responses compared to some competitors. Available to X Premium subscribers and via the Grok standalone app.
Generative AI
AI that can create new content — text, images, audio, video, code — rather than just analysing or classifying existing content. ChatGPT generating a business proposal, DALL-E creating an image from a description, and GitHub Copilot writing code are all examples of generative AI in action.
GPT (Generative Pre-trained Transformer)
The type of AI model that powers ChatGPT, developed by OpenAI. GPT models are trained on vast amounts of text data and can generate human-like text, answer questions, summarize documents, write code, and much more. The 'transformer' part refers to the underlying architecture that makes modern AI so powerful.
Guardrails
Rules, filters, and safety mechanisms built into AI systems to prevent them from producing harmful, offensive, or inappropriate outputs. Guardrails are why AI assistants refuse certain requests. When building custom AI tools for businesses, we also add business-specific guardrails — for example, preventing a customer service bot from making promises the business can't keep.
Hallucination
When an AI confidently produces information that is factually incorrect or completely made up. This is one of the most important limitations of current AI tools to understand. AI doesn't 'know' things the way humans do — it predicts likely text, which can sometimes result in plausible-sounding but wrong answers. Always verify important facts.
Human-in-the-Loop
A system design where humans are involved in reviewing, approving, or correcting AI outputs before they're acted on. For most business applications, keeping a human in the loop is best practice — especially for anything customer-facing, financial, or legally sensitive.
Hyperparameter
Settings that control how an AI model is trained — like how fast it learns, how many training rounds it goes through, or how large its layers are. Unlike regular parameters (which the model learns automatically), hyperparameters are set by the people building the model. Getting them right is part of the craft of AI development.
Inference
The process of using a trained AI model to generate outputs from new inputs. When you type a question into ChatGPT and it responds, that's inference — the model is applying what it learned during training to your specific input. Training happens once; inference happens every time you use the tool.
Integration
Connecting two or more software systems so they can share data and work together. In AI, integration typically means connecting an AI tool to your existing business software — like linking a chatbot to your CRM, or connecting an automation tool to your booking system.
Jailbreak
An attempt to bypass the safety guidelines or restrictions built into an AI model — usually by crafting clever prompts that trick the AI into ignoring its rules. AI companies work continuously to prevent jailbreaks. As a business owner, it's worth knowing this exists so you understand why AI tools sometimes refuse certain requests — those guardrails are there for good reason.
Knowledge Base
A structured collection of information that an AI can draw on to answer questions. When we build a custom AI assistant for your business, we often create a knowledge base containing your FAQs, product details, policies, and procedures — so the AI gives accurate, business-specific answers rather than generic ones.
Large Language Model (LLM)
An AI model trained on enormous amounts of text data that can understand and generate human language. ChatGPT, Claude, and Gemini are all LLMs. They're called 'large' because of the sheer scale of data they're trained on and the number of parameters (internal settings) they contain — often in the billions.
Llama
Meta's family of open-source large language models, released for anyone to download, run, and build on. Unlike ChatGPT or Claude, Llama models can be run locally on your own hardware without sending data to a third-party server — making them popular for privacy-sensitive applications and businesses that want more control over their AI stack.
Machine Learning (ML)
A branch of AI where systems learn from data and improve their performance over time without being explicitly programmed for every scenario. Instead of following rigid rules, machine learning models identify patterns in data and use those patterns to make predictions or decisions.
Multimodal AI
AI that can process and generate multiple types of content — text, images, audio, and video — rather than just one. GPT-4o is multimodal: you can show it a photo and ask a question about it, or have it describe an image. Multimodal AI opens up a much wider range of business applications than text-only models.
Model
In AI, a model is the trained system that takes inputs and produces outputs. When people refer to 'the model', they mean the AI itself — the mathematical structure that has been trained on data. Different models have different strengths, weaknesses, and use cases.
Natural Language Processing (NLP)
The branch of AI focused on enabling computers to understand, interpret, and generate human language. NLP powers chatbots, voice assistants, translation tools, sentiment analysis, and much more. When an AI understands what you're asking in plain English, that's NLP at work.
Neural Network
A type of AI system loosely inspired by the structure of the human brain, made up of layers of interconnected nodes that process information. Neural networks are the foundation of most modern AI — including image recognition, speech recognition, and large language models.
No-Code / Low-Code
Tools and platforms that allow people to build software applications and automations without writing traditional code. Many AI tools are now available in no-code or low-code formats, making them accessible to business owners without technical backgrounds. Zapier and Make are popular examples.
OpenAI
The company behind ChatGPT, GPT-4, and DALL-E. OpenAI is one of the most influential AI research organizations in the world. Founded in 2015, it has become the most recognized name in consumer AI and provides the underlying technology for many third-party AI tools and applications.
Overfitting
When an AI model learns its training data too well — including the noise and quirks — and as a result performs poorly on new, real-world data. Think of it like a student who memorises past exam papers word-for-word but can't answer a question that's phrased slightly differently. Overfitting is a common challenge in building reliable AI systems.
Parameters
The internal settings of an AI model that are adjusted during training to improve performance. A model with more parameters is generally more capable but also more expensive to run. GPT-4 is estimated to have hundreds of billions of parameters — which is part of why it's so capable.
Prompt
The input you give to an AI model — the question, instruction, or context that tells the AI what you want it to do. Writing effective prompts (prompt engineering) is a skill that significantly affects the quality of AI outputs. A well-crafted prompt can be the difference between a mediocre and an excellent result.
Prompt Engineering
The practice of designing and refining prompts to get better outputs from AI models. It involves understanding how AI models interpret instructions and structuring your inputs to guide the model toward the response you want. Good prompt engineering is one of the most practical AI skills a business owner can develop.
Prompt Injection
A type of attack where a malicious user crafts inputs designed to override an AI system's instructions and make it behave in unintended ways. For example, a user might try to trick a customer service chatbot into ignoring its rules by embedding hidden instructions in their message. It's a real security concern for businesses deploying AI-facing tools — and something we account for when building AI systems for clients.
Query
A question or request submitted to an AI system, search engine, or database. In everyday AI use, a query is essentially what you type into ChatGPT or a search bar. In more technical contexts, queries are used to retrieve specific data from databases. The quality of your query directly affects the quality of the answer you get back.
RAG (Retrieval-Augmented Generation)
A technique that combines an AI language model with a search system, allowing the AI to retrieve relevant information from a specific knowledge base before generating a response. RAG is how you build AI tools that can answer questions about your specific business — your products, policies, FAQs — rather than just general knowledge.
Sentiment Analysis
An AI technique that identifies and categorises the emotional tone of text — positive, negative, or neutral. Businesses use sentiment analysis to monitor customer reviews, social media mentions, and support tickets to understand how customers feel about their products or services at scale.
Shadow AI
The use of AI tools by employees without the knowledge or approval of their organization's IT or management team. Just as 'shadow IT' described unauthorised software use, shadow AI is a growing concern — staff may be pasting sensitive client data into ChatGPT or using unapproved AI tools, creating data privacy and security risks. Having a clear AI policy in your business is increasingly important.
System Prompt
A set of instructions given to an AI model before a conversation begins, used to define its persona, rules, and behavior. When we build a custom AI chatbot for a business, the system prompt is where we tell it who it is, what it can and can't do, and how it should respond to customers.
Token
The basic unit of text that AI language models process. A token is roughly equivalent to a word or part of a word — 'hamburger' might be split into two tokens, while 'the' is one. AI models have token limits for inputs and outputs, and usage is often billed per token. Understanding tokens helps you use AI tools more efficiently.
Training
The process of teaching an AI model by exposing it to large amounts of data and adjusting its parameters to improve performance. Training a large AI model requires enormous computing power and can take weeks or months. Most businesses use pre-trained models rather than training their own from scratch.
Transformer
The underlying architecture that powers most modern AI language models, including GPT, Claude, and Gemini. Introduced by Google in 2017, the transformer architecture revolutionised AI by enabling models to process and understand context across long sequences of text far more effectively than previous approaches.
Use Case
A specific, practical application of a technology to solve a real problem. In AI, a use case might be 'using AI to automatically respond to common customer enquiries' or 'using AI to summarize meeting notes'. Identifying the right use cases for your business is the first step in any successful AI implementation.
Vector Database
A type of database designed to store and search data in the format that AI models use (vectors/embeddings). Vector databases are a key component of RAG systems — they allow AI tools to quickly find the most relevant information from a large knowledge base when answering a question.
Vibe Coding
A term coined in 2025 describing the practice of building software by describing what you want in plain English to an AI — and letting the AI write the code for you. Instead of learning to code, you 'vibe' with the AI: you describe the idea, review what it builds, give feedback, and iterate. Tools like Cursor, GitHub Copilot, and platforms like the one this website was built on make vibe coding accessible to non-developers. It's one of the most significant shifts in how software gets made.
Voice AI
AI systems that can understand and generate spoken language — enabling voice-based interactions with software. Voice AI powers smart speakers (Alexa, Siri, Google Assistant), AI phone receptionists, and voice-enabled customer service tools. For small businesses, voice AI can handle inbound calls, answer common questions, and route enquiries — 24 hours a day.
Workflow
A sequence of steps or tasks that make up a business process. In the context of AI and automation, a workflow is the series of actions that get triggered and executed automatically — for example, a new lead fills in a form, which triggers an AI to qualify them, send a welcome email, and add them to your CRM.
xAI
The AI company founded by Elon Musk in 2023, responsible for building Grok. xAI's stated mission is to understand the true nature of the universe through AI research. It's one of the newer major players in the AI space and is notable for its integration with the X (formerly Twitter) platform.
Yield (AI Output Quality)
In AI contexts, yield refers to the quality and usefulness of the outputs an AI produces relative to the inputs given. High-yield prompting means getting consistently valuable, accurate, and relevant responses. Improving your yield is largely about better prompts, clearer context, and choosing the right model for the task.
Zero-Shot Learning
The ability of an AI model to perform a task it hasn't been specifically trained on, using only a description or instruction. For example, asking ChatGPT to classify customer feedback into categories it's never seen before. Zero-shot capability is one of the things that makes modern LLMs so versatile for business use.
AI Washing
When a company exaggerates or falsely claims that its product uses AI to appear more innovative or attract investment. Similar to 'greenwashing' in sustainability. As AI becomes a buzzword, AI washing is increasingly common — it's worth asking vendors exactly how AI is used in their product, not just whether it is.
Algorithm
A set of step-by-step instructions a computer follows to complete a task or solve a problem. Think of it like a recipe — the computer follows each step in order to produce a result. Most AI tools are built on complex algorithms that process data and generate outputs.
Artificial Intelligence (AI)
Technology that enables computers to perform tasks that normally require human intelligence — things like understanding language, recognizing images, making decisions, and learning from experience. AI is an umbrella term that covers many different technologies and approaches.
Automation
Using technology to perform tasks with little or no human input. In a business context, automation means setting up systems that handle repetitive work automatically — like sending follow-up emails, updating spreadsheets, or routing customer enquiries — so your team can focus on higher-value work.
Agentic AI
AI that can take actions and make decisions autonomously to complete a goal — not just answer questions, but actually do things like browse the web, send emails, or interact with software on your behalf. Agentic AI is one of the fastest-growing areas of AI development.
AI-Native
A product, business, or workflow that is built from the ground up with AI at its core — rather than having AI bolted on as an afterthought. An AI-native business uses AI as a fundamental part of how it operates, not just as an occasional tool. Increasingly, being AI-native is a competitive advantage.
API (Application Programming Interface)
A way for two software applications to talk to each other. When we integrate an AI tool with your existing software, we're usually using APIs to connect them. You don't need to understand how APIs work — just know they're the plumbing that makes integrations possible.
ChatGPT
The AI assistant built by OpenAI and one of the most widely used AI tools in the world. ChatGPT can write, summarize, answer questions, draft emails, generate ideas, and much more — all through a simple chat interface. It runs on OpenAI's GPT models and is available free or via a paid plan. For most small businesses, it's the best starting point for exploring what AI can do.
Claude
An AI assistant built by Anthropic, widely regarded as one of the best alternatives to ChatGPT. Claude is known for nuanced writing, long-document analysis, and following complex instructions carefully. It's designed with a strong focus on safety and honesty. Available at claude.ai with a free tier and paid plans.
Bot
Short for 'robot' — a software program that performs automated tasks. In business, bots are commonly used for customer service (chatbots), data collection, and repetitive admin tasks. Not all bots use AI, but AI-powered bots are far more capable than simple rule-based ones.
Benchmark
A standardised test used to measure and compare the performance of AI models. When you see claims like 'our AI scores 90% on X benchmark', it means the model was tested against a standard set of problems. Benchmarks help compare different AI tools, though real-world performance can vary.
Black Box
A system where you can see the inputs and outputs but not what happens in between. Most AI models are black boxes — you give them a prompt and get a response, but the internal reasoning is not visible or easily explainable. This is why 'explainable AI' is an active area of research, particularly in regulated industries like finance and healthcare.
Chatbot
A software program designed to simulate conversation with humans, typically via text. Modern AI-powered chatbots (like those built on ChatGPT) can understand natural language, answer complex questions, and handle customer service tasks around the clock — far beyond the simple 'press 1 for billing' bots of the past.
Context Window
The amount of text an AI model can 'see' and consider at one time. Think of it like short-term memory — the larger the context window, the more of a conversation or document the AI can take into account when generating a response. Larger context windows generally produce more coherent, relevant outputs.
Copilot
An AI assistant that works alongside a human to help them do their job better — not replacing them, but augmenting their capabilities. Microsoft's Copilot (built into Office 365) is a well-known example. The term reflects the idea that the human is still in control; the AI is just helping navigate.
Data
Information in a form that computers can process. AI systems learn from data — the more relevant, high-quality data an AI is trained on, the better it tends to perform. In a business context, your data includes things like customer records, transaction history, emails, and documents.
Deepfake
AI-generated video, audio, or images that realistically depict someone saying or doing something they never actually said or did. Deepfakes are created using deep learning techniques and have become increasingly convincing. As a business owner, it's important to be aware of deepfakes — both as a potential security risk (e.g. fake audio of a CEO authorising a transfer) and as a reputational concern.
Deep Learning
A type of machine learning that uses neural networks with many layers to learn from large amounts of data. Deep learning powers many of the most impressive AI capabilities today — including image recognition, speech recognition, and large language models like ChatGPT.
Embedding
A way of representing text, images, or other data as numbers so that an AI can understand relationships between concepts. Embeddings allow AI to understand that 'dog' and 'puppy' are related, or that 'invoice' and 'bill' mean similar things — even without being explicitly told.
Emergent Behavior
Unexpected capabilities that appear in AI models as they grow larger or are trained on more data — abilities that weren't explicitly programmed or anticipated. For example, large language models spontaneously developed the ability to do basic arithmetic and translate languages without being specifically trained to do so. Emergent behavior is one of the reasons AI development can be unpredictable.
Fine-Tuning
The process of taking a pre-trained AI model and training it further on a specific dataset to make it better at a particular task. For example, fine-tuning a general language model on your company's documents and tone of voice so it writes in your style and understands your business context.
Frontier Model
The most advanced AI models available at any given time — the cutting edge of what's technically possible. GPT-4o, Claude 3.5 Sonnet, and Gemini Ultra are examples of frontier models. The term is used in AI policy and safety discussions to describe models powerful enough to potentially pose novel risks alongside their capabilities.
Foundation Model
A large AI model trained on a broad dataset that can be adapted for many different tasks. GPT-4, Claude, and Gemini are all foundation models. They're called 'foundation' models because they serve as the base that other, more specialized AI applications are built on top of.
Gemini
Google's flagship AI assistant and language model, available at gemini.google.com. Gemini integrates natively with Google Workspace — Gmail, Docs, Sheets, Drive — making it a strong choice for businesses already running on Google tools. It competes directly with ChatGPT and Claude and is available with a free tier and paid Google One AI Premium plan.
Grok
An AI assistant built by xAI, Elon Musk's AI company, and integrated into the X (formerly Twitter) platform. Grok is notable for having real-time access to information posted on X and for taking a less filtered approach to responses compared to some competitors. Available to X Premium subscribers and via the Grok standalone app.
Generative AI
AI that can create new content — text, images, audio, video, code — rather than just analysing or classifying existing content. ChatGPT generating a business proposal, DALL-E creating an image from a description, and GitHub Copilot writing code are all examples of generative AI in action.
GPT (Generative Pre-trained Transformer)
The type of AI model that powers ChatGPT, developed by OpenAI. GPT models are trained on vast amounts of text data and can generate human-like text, answer questions, summarize documents, write code, and much more. The 'transformer' part refers to the underlying architecture that makes modern AI so powerful.
Guardrails
Rules, filters, and safety mechanisms built into AI systems to prevent them from producing harmful, offensive, or inappropriate outputs. Guardrails are why AI assistants refuse certain requests. When building custom AI tools for businesses, we also add business-specific guardrails — for example, preventing a customer service bot from making promises the business can't keep.
Hallucination
When an AI confidently produces information that is factually incorrect or completely made up. This is one of the most important limitations of current AI tools to understand. AI doesn't 'know' things the way humans do — it predicts likely text, which can sometimes result in plausible-sounding but wrong answers. Always verify important facts.
Human-in-the-Loop
A system design where humans are involved in reviewing, approving, or correcting AI outputs before they're acted on. For most business applications, keeping a human in the loop is best practice — especially for anything customer-facing, financial, or legally sensitive.
Hyperparameter
Settings that control how an AI model is trained — like how fast it learns, how many training rounds it goes through, or how large its layers are. Unlike regular parameters (which the model learns automatically), hyperparameters are set by the people building the model. Getting them right is part of the craft of AI development.
Inference
The process of using a trained AI model to generate outputs from new inputs. When you type a question into ChatGPT and it responds, that's inference — the model is applying what it learned during training to your specific input. Training happens once; inference happens every time you use the tool.
Integration
Connecting two or more software systems so they can share data and work together. In AI, integration typically means connecting an AI tool to your existing business software — like linking a chatbot to your CRM, or connecting an automation tool to your booking system.
Jailbreak
An attempt to bypass the safety guidelines or restrictions built into an AI model — usually by crafting clever prompts that trick the AI into ignoring its rules. AI companies work continuously to prevent jailbreaks. As a business owner, it's worth knowing this exists so you understand why AI tools sometimes refuse certain requests — those guardrails are there for good reason.
Knowledge Base
A structured collection of information that an AI can draw on to answer questions. When we build a custom AI assistant for your business, we often create a knowledge base containing your FAQs, product details, policies, and procedures — so the AI gives accurate, business-specific answers rather than generic ones.
Large Language Model (LLM)
An AI model trained on enormous amounts of text data that can understand and generate human language. ChatGPT, Claude, and Gemini are all LLMs. They're called 'large' because of the sheer scale of data they're trained on and the number of parameters (internal settings) they contain — often in the billions.
Llama
Meta's family of open-source large language models, released for anyone to download, run, and build on. Unlike ChatGPT or Claude, Llama models can be run locally on your own hardware without sending data to a third-party server — making them popular for privacy-sensitive applications and businesses that want more control over their AI stack.
Machine Learning (ML)
A branch of AI where systems learn from data and improve their performance over time without being explicitly programmed for every scenario. Instead of following rigid rules, machine learning models identify patterns in data and use those patterns to make predictions or decisions.
Multimodal AI
AI that can process and generate multiple types of content — text, images, audio, and video — rather than just one. GPT-4o is multimodal: you can show it a photo and ask a question about it, or have it describe an image. Multimodal AI opens up a much wider range of business applications than text-only models.
Model
In AI, a model is the trained system that takes inputs and produces outputs. When people refer to 'the model', they mean the AI itself — the mathematical structure that has been trained on data. Different models have different strengths, weaknesses, and use cases.
Natural Language Processing (NLP)
The branch of AI focused on enabling computers to understand, interpret, and generate human language. NLP powers chatbots, voice assistants, translation tools, sentiment analysis, and much more. When an AI understands what you're asking in plain English, that's NLP at work.
Neural Network
A type of AI system loosely inspired by the structure of the human brain, made up of layers of interconnected nodes that process information. Neural networks are the foundation of most modern AI — including image recognition, speech recognition, and large language models.
No-Code / Low-Code
Tools and platforms that allow people to build software applications and automations without writing traditional code. Many AI tools are now available in no-code or low-code formats, making them accessible to business owners without technical backgrounds. Zapier and Make are popular examples.
OpenAI
The company behind ChatGPT, GPT-4, and DALL-E. OpenAI is one of the most influential AI research organizations in the world. Founded in 2015, it has become the most recognized name in consumer AI and provides the underlying technology for many third-party AI tools and applications.
Overfitting
When an AI model learns its training data too well — including the noise and quirks — and as a result performs poorly on new, real-world data. Think of it like a student who memorises past exam papers word-for-word but can't answer a question that's phrased slightly differently. Overfitting is a common challenge in building reliable AI systems.
Parameters
The internal settings of an AI model that are adjusted during training to improve performance. A model with more parameters is generally more capable but also more expensive to run. GPT-4 is estimated to have hundreds of billions of parameters — which is part of why it's so capable.
Prompt
The input you give to an AI model — the question, instruction, or context that tells the AI what you want it to do. Writing effective prompts (prompt engineering) is a skill that significantly affects the quality of AI outputs. A well-crafted prompt can be the difference between a mediocre and an excellent result.
Prompt Engineering
The practice of designing and refining prompts to get better outputs from AI models. It involves understanding how AI models interpret instructions and structuring your inputs to guide the model toward the response you want. Good prompt engineering is one of the most practical AI skills a business owner can develop.
Prompt Injection
A type of attack where a malicious user crafts inputs designed to override an AI system's instructions and make it behave in unintended ways. For example, a user might try to trick a customer service chatbot into ignoring its rules by embedding hidden instructions in their message. It's a real security concern for businesses deploying AI-facing tools — and something we account for when building AI systems for clients.
Query
A question or request submitted to an AI system, search engine, or database. In everyday AI use, a query is essentially what you type into ChatGPT or a search bar. In more technical contexts, queries are used to retrieve specific data from databases. The quality of your query directly affects the quality of the answer you get back.
RAG (Retrieval-Augmented Generation)
A technique that combines an AI language model with a search system, allowing the AI to retrieve relevant information from a specific knowledge base before generating a response. RAG is how you build AI tools that can answer questions about your specific business — your products, policies, FAQs — rather than just general knowledge.
Sentiment Analysis
An AI technique that identifies and categorises the emotional tone of text — positive, negative, or neutral. Businesses use sentiment analysis to monitor customer reviews, social media mentions, and support tickets to understand how customers feel about their products or services at scale.
Shadow AI
The use of AI tools by employees without the knowledge or approval of their organization's IT or management team. Just as 'shadow IT' described unauthorised software use, shadow AI is a growing concern — staff may be pasting sensitive client data into ChatGPT or using unapproved AI tools, creating data privacy and security risks. Having a clear AI policy in your business is increasingly important.
System Prompt
A set of instructions given to an AI model before a conversation begins, used to define its persona, rules, and behavior. When we build a custom AI chatbot for a business, the system prompt is where we tell it who it is, what it can and can't do, and how it should respond to customers.
Token
The basic unit of text that AI language models process. A token is roughly equivalent to a word or part of a word — 'hamburger' might be split into two tokens, while 'the' is one. AI models have token limits for inputs and outputs, and usage is often billed per token. Understanding tokens helps you use AI tools more efficiently.
Training
The process of teaching an AI model by exposing it to large amounts of data and adjusting its parameters to improve performance. Training a large AI model requires enormous computing power and can take weeks or months. Most businesses use pre-trained models rather than training their own from scratch.
Transformer
The underlying architecture that powers most modern AI language models, including GPT, Claude, and Gemini. Introduced by Google in 2017, the transformer architecture revolutionised AI by enabling models to process and understand context across long sequences of text far more effectively than previous approaches.
Use Case
A specific, practical application of a technology to solve a real problem. In AI, a use case might be 'using AI to automatically respond to common customer enquiries' or 'using AI to summarize meeting notes'. Identifying the right use cases for your business is the first step in any successful AI implementation.
Vector Database
A type of database designed to store and search data in the format that AI models use (vectors/embeddings). Vector databases are a key component of RAG systems — they allow AI tools to quickly find the most relevant information from a large knowledge base when answering a question.
Vibe Coding
A term coined in 2025 describing the practice of building software by describing what you want in plain English to an AI — and letting the AI write the code for you. Instead of learning to code, you 'vibe' with the AI: you describe the idea, review what it builds, give feedback, and iterate. Tools like Cursor, GitHub Copilot, and platforms like the one this website was built on make vibe coding accessible to non-developers. It's one of the most significant shifts in how software gets made.
Voice AI
AI systems that can understand and generate spoken language — enabling voice-based interactions with software. Voice AI powers smart speakers (Alexa, Siri, Google Assistant), AI phone receptionists, and voice-enabled customer service tools. For small businesses, voice AI can handle inbound calls, answer common questions, and route enquiries — 24 hours a day.
Workflow
A sequence of steps or tasks that make up a business process. In the context of AI and automation, a workflow is the series of actions that get triggered and executed automatically — for example, a new lead fills in a form, which triggers an AI to qualify them, send a welcome email, and add them to your CRM.
xAI
The AI company founded by Elon Musk in 2023, responsible for building Grok. xAI's stated mission is to understand the true nature of the universe through AI research. It's one of the newer major players in the AI space and is notable for its integration with the X (formerly Twitter) platform.
Yield (AI Output Quality)
In AI contexts, yield refers to the quality and usefulness of the outputs an AI produces relative to the inputs given. High-yield prompting means getting consistently valuable, accurate, and relevant responses. Improving your yield is largely about better prompts, clearer context, and choosing the right model for the task.
Zero-Shot Learning
The ability of an AI model to perform a task it hasn't been specifically trained on, using only a description or instruction. For example, asking ChatGPT to classify customer feedback into categories it's never seen before. Zero-shot capability is one of the things that makes modern LLMs so versatile for business use.
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