AI glossary

A program that uses a large language model to plan, take actions through tools, and pursue a goal across multiple steps — not just reply once.

An AI assistant is a conversational tool that answers one request at a time and forgets the work between sessions. An AI agent takes a goal, plans the steps, and carries work forward using tools and shared context. For creative teams the difference is who holds the context: the person, or the system.

A repeatable test suite for an AI feature — a set of inputs, expected behaviour, and a way to score how often the system gets it right.

The coordination layer that directs multiple AI models, tools, and steps toward one goal, deciding what runs, in what order, with which context, and when a human steps in.

An AI workflow is a repeatable sequence of AI steps, from trigger to finished output, that carries a task through without a person running each step by hand.

The discipline of deciding what information to put in front of an AI system at the right time — and what to leave out — so it can do useful work.

The amount of text, measured in tokens, that an AI model can consider at once. It covers the prompt, any documents or history you include, and the model's own reply.

Numeric vectors that represent the meaning of a piece of text, image, or other content — used so a computer can compare two things by semantic similarity, not just keyword overlap.

Continuing to train a base model on a smaller, curated dataset so it adapts to a specific style, format, or domain.

When a language model generates plausible-sounding text that is factually wrong — a made-up citation, a fabricated number, a confidently invented detail.

A neural network trained on huge volumes of text to predict the next token, used today as the engine behind chat, agents, and most generative-AI features.

An open standard for connecting AI applications to tools, data sources, and systems — letting one model talk to many tools without bespoke integrations.

The craft of writing the instructions you give a language model — choosing wording, examples, structure, and constraints to get useful, repeatable output.

A pattern that pulls relevant documents into the prompt at inference time so a model can answer questions about information it was never trained on.

Tool use is how an AI model does more than talk. It calls external functions, APIs, and software to fetch data and take actions, then uses the results to produce real work rather than just text.