Plain English
The AI glossary
Every term the guide leans on, defined the way we would explain it across a desk. No prior AI background assumed, and no term needed before you start: read these as you meet them.
- Large language model (LLM)
The text-predicting model behind almost every AI assistant
- AI agent
AI that works a goal in steps, not one answer
- Prompt
Your request, and the one skill worth practising first
- Token
The chunk of text that limits and pricing are counted in
- Context window
How much a model can hold in mind at once
- Hallucination
Confident, wrong, and the reason you still check
- RAG (retrieval-augmented generation)
Answering from your documents instead of general training
- MCP (Model Context Protocol)
The open standard letting assistants reach your other tools
79 terms, plain English, checked July 2026
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Software that does work we used to assume needed a person, such as writing, summarising, reading images or judging what a request means.
A model trained on very large amounts of text to predict what comes next, which is what lets it write, summarise, translate and answer questions.
The trained file at the centre of an AI product, holding what it learnt from its training data, which the chat window or app is wrapped around.
A large general-purpose model trained once at great expense, then adapted by many companies for many jobs rather than built fresh for each one.
A model trained to work a problem through in steps before answering, which helps on maths, logic and planning at the cost of speed.
An AI system that works towards a goal in steps rather than answering a single question: it can plan, use tools such as search or a calendar, check its own results and try again.
A hypothetical AI able to match a capable person across most intellectual work rather than in narrow areas, with no agreement on whether or when it arrives.
Able to work with more than one kind of input or output: text, images, audio, sometimes video.
The field concerned with getting software to interpret what is in an image or video: objects, faces, defects, movement.
Producing a new picture from a written description rather than retrieving or editing an existing one.
The kind of model behind most image generators, which starts from visual noise and refines it step by step towards the picture your description asks for.
AI you speak to and that speaks back, joining speech recognition, a language model and synthetic speech into one conversation.
Turning recorded or live speech into written words, which is what produces a meeting transcript or live captions.
Turning written words into spoken audio, used for narration, accessibility and assistants that answer out loud.
Reading the text inside a picture of a page, so a scan or a photograph becomes words a computer can search and copy.
The text, images and code a model learnt from: much of the public web, books, licensed sources, and sometimes users' conversations.
The long first stage of building a model, where it learns general patterns from an enormous body of material before anyone teaches it to be helpful.
Taking a trained model and training it further on a smaller, specific set of examples: your firm's writing style, a legal domain, a support script.
Reusing what a model learnt on one large task as the starting point for a different, usually smaller one, instead of starting from nothing.
Training by trial and reward rather than by copying examples: the system tries something, gets scored, and shifts towards what scored well.
The stage where people rank a model's competing answers and it is trained towards the ones they preferred, which is much of why assistants feel helpful.
Training a smaller model to imitate a larger one, to get most of the quality at a fraction of the cost and wait.
Storing a model's numbers at lower precision so it needs less memory and can run on smaller hardware, giving up a little accuracy.
Training material generated rather than collected, used where real examples are scarce, sensitive or expensive to label.
The kind of chip AI runs on, built to do enormous numbers of simple calculations at once, which is exactly the shape of the work models do.
What you type to an AI assistant: the request plus any context you include.
Standing instructions given to a model before your conversation starts, usually by the tool's developer: its role, tone, rules and boundaries.
The practice of writing and refining requests to get reliable results, which in practice looks more like briefing well than like engineering.
Showing a model two or three worked examples inside your request so it can match the pattern you want.
Asking for something with no worked examples at all, relying on what the model already learnt.
The amount of text a model can consider at once: your conversation, any documents you attach, and its own replies.
The unit models actually read and write: a short chunk of text, roughly three-quarters of an English word on average.
A setting that controls how predictable a model's output is.
A feature that lets an assistant carry facts about you across separate conversations, rather than starting blank every time.
When a model states something false as if it were certain: an invented citation, a wrong date, a plausible-sounding statistic.
Tying a model's answer to a specific, checkable source: a search result, your uploaded document, a database record.
The link or reference an AI answer offers as the source of a claim, which is only useful once you open it and check.
The intermediate reasoning a model works through before giving its answer, sometimes shown to you as thinking.
The hidden working a reasoning model produces on its way to an answer, which you usually pay for and count against limits without ever seeing it.
Before answering, the system searches a chosen set of documents, then writes its reply from what it found.
A way of representing text (or images) as a long list of numbers so that similar meanings end up numerically close together.
A database built to store embeddings and find the closest matches quickly, even across millions of documents.
Search that matches on meaning rather than on the exact words typed, so a query about annual leave finds the holiday policy.
The defined set of documents an assistant is allowed to answer from: policies, manuals, past tickets, whatever you have chosen to load.
A sequence of steps that runs in a set order, with each step's output feeding the next, whether people or software do the work.
Handing a repeated task to software so it runs on a trigger instead of waiting for someone to remember it.
Coordinating many automated steps across different systems, including the order they run in, what happens when one fails, and what gets retried.
An open standard for building connectors, so one connector can work across different assistants instead of being rebuilt for each.
Connectors let an AI assistant reach into your other tools: read a shared drive, check a calendar, update a spreadsheet.
An add-on that extends a tool you already use, rather than a separate product you open on its own.
An application programming interface: the route by which one piece of software talks to another.
A ready-made bundle of code a provider publishes so developers can use its API without writing the plumbing themselves.
A message one system sends to another the moment something happens, so the second system can react without repeatedly asking whether anything has changed.
The mechanism by which a model asks to run something outside itself, such as a search or a calculation, and then uses the result in its answer.
Making a model answer in a fixed shape with named fields, so another system can read the reply instead of a person having to.
A setting that forces a model's reply into JSON, the plain-text format software uses to pass data around.
Designing an automated process so a person approves or corrects specific steps rather than only seeing the result.
A standard test used to score models against each other, useful for shortlisting and unreliable as a promise about your own work.
The wait between sending a request and getting the answer.
Running a trained model to produce an answer, as opposed to training it in the first place.
What it costs to run a model each time it answers, which is why heavy use is metered and the strongest models are rationed.
A cap on how many requests you may send in a given period, which protects the service and shapes how fast an automated job can run.
The ceiling a plan puts on how much you can do in a day, week or month before you are slowed, switched to a weaker model, or stopped.
Paying for what you actually use rather than a flat subscription, which suits uneven demand and makes forecasting harder.
A free tier is a usable product at no cost, with limits such as message caps or slower models; freemium describes the business model built around it, where the free version exists to sell the paid one.
Running an AI model on computers you control, whether your own servers or a private cloud, instead of sending data to a provider's service.
A model running on the machine in front of you, so what you type never leaves it.
AI that runs on or beside the device producing the data, such as a phone, camera or vehicle, rather than sending it away to be processed.
AI that runs on a provider's servers and reaches you over the internet, which is how nearly every well-known assistant works.
Software whose code is published under a licence that lets anyone read, change and redistribute it.
A model whose trained parameters are published for anyone to download and run, unlike closed models used only through a provider's service.
A model you can only use through its owner's service, with the trained file itself never released.
What happens to the text, files and recordings you put into an AI tool: who can see them, how long they are kept, and whether they train the model.
A setting that tells a provider not to use your conversations to train its models.
The work of making AI systems behave predictably and within bounds, covering both everyday failures and longer-term concerns.
The problem of getting a system to pursue what people actually intended rather than the literal instruction it was given.
Rules and filters wrapped around a model to keep its output within bounds: refusing harmful requests, staying on topic, not revealing private data.
Deliberately attacking an AI system before its users do: probing for ways to make it leak data, produce harmful content or ignore its instructions.
A request crafted to get a model to ignore its own rules, and the reason guardrails are treated as a control rather than a guarantee.
Terms are step one. When you want the tools rather than the vocabulary, the guide answers that side.
Last checked July 2026
How these definitions are written and checked: how we review.