A plain-English AI glossary

A quick reference for the words that get thrown around. Each is defined plainly, with a pointer to the fuller explanation where there is one.

The basics

• Large language model (LLM): a program that predicts text one chunk at a time; the engine behind Claude, ChatGPT, and Copilot. See the full explainer.
• Token: a short chunk of text the model reads and writes in; roughly ¾ of a word. See tokens and context.
• Context window: how much text the model can consider at once. Older parts of a long chat fall out of view.
• Prompt: what you type in. Better prompts get better answers.

How it learns

• Training: the process of tuning the model on lots of text. See how a model is trained.
• Training cut-off: the date after which the model knows nothing first-hand.
• Hallucination: a confident answer that simply isn’t true. The reason you verify.
• Fine-tuning: extra training that specialises a model for a task or a tone.

Going further

• Embedding: text turned into numbers that capture meaning. See embeddings and RAG.
• RAG: answering from your own documents by retrieving relevant chunks first.
• Agent: a model given tools and a goal so it can take actions, not just answer. See AI agents explained.
• Prompt injection: hidden instructions that try to hijack a model. See the security explainer.

If a term here is new to you, follow its link before reading on. The rest of the guide assumes these.