AI glossary
Definitions written for someone who has to make a decision, not pass an exam. Each entry says what the term means, when it matters and what it does not solve.
A
Agent
A model given tools and a goal, allowed to decide its own next steps in a loop rather than answering once.
C
Chain of thought CoT
Prompting a model to write out its intermediate steps before the final answer.
Context window
The maximum amount of text, measured in tokens, that a model can take into account at once - your prompt and its answer together.
E
Embeddings
Numeric vectors representing text, where similar meanings land close together - the basis of semantic search.
F
Few-shot prompting
Showing the model two or three worked examples of the task inside the prompt, instead of only describing it.
Fine-tuning
Further training of an existing model on your own examples, to change its behaviour rather than its knowledge.
H
Hallucination
A confident, fluent answer that is simply wrong - an invented citation, function, statistic or fact.
I
Inference
Running a trained model to produce output - what happens every time you send a prompt.
L
Large language model LLM
A model trained on large amounts of text to predict the next token, which in practice lets it write, summarise, translate and reason over text.
M
Multimodal
A model that accepts or produces more than one kind of input - text plus images, audio or video.
P
Prompt injection
An attack where instructions hidden in content the model reads get executed as if you had written them.
Q
Quantisation
Storing a model with lower-precision numbers so it fits in less memory and runs on ordinary hardware.
R
Reasoning model
A model trained to spend extra computation working through a problem before answering.
Retrieval-augmented generation RAG
A pattern where relevant documents are fetched first and pasted into the prompt, so the model answers from your data rather than its training.
S
System prompt
Instructions given to the model outside the conversation, which apply to every turn rather than one message.
T
Temperature
A sampling setting that controls how much randomness goes into choosing each next token.
Token
The unit a model reads and writes - roughly a word fragment. Pricing, context limits and speed are all measured in tokens.
V
Vector database
A store built for finding the nearest vectors to a query vector quickly, across millions of items.