AI glossary

A

Basics AI assistant An AI system that helps a user with tasks through conversation, such as writing, searching, analysing or coding. Basics API (application programming interface) A way for software to call a service programmatically; AI providers sell model access through APIs billed per use. Basics Artificial intelligence (AI) The field of building computer systems that perform tasks normally requiring human intelligence, such as understanding language, recognising images or making decisions.

B

Basics Benchmark A standard test used to measure and compare model abilities, such as coding, maths or knowledge.

C

Basics Chatbot A program that converses with people in natural language, today usually powered by a large language model. Basics Closed (proprietary) model A model whose weights are not published and which can only be used through the provider's API or apps. Basics Completion (output) The text a model generates in response to a prompt. Output tokens usually cost several times more than input tokens. Basics Context window The maximum number of tokens a model can consider at once, counting both your input and its output.

D

Basics Dataset A collection of examples used to train or evaluate a model. Basics Deep learning Machine learning using neural networks with many layers, which learn increasingly abstract features from raw data.

F

Basics Foundation model A large model trained on broad data that can be adapted to many different tasks. Basics Frontier model One of the most capable models available at a given time, at the edge of what AI can currently do.

G

Basics Generative AI AI that creates new content, such as text, images, audio, video or code, rather than only classifying or predicting.

H

Basics Hallucination When a model states something false or invented with apparent confidence.

I

Basics Inference Running a trained model to produce an output; what you pay for when you call an AI API.

K

Basics Knowledge cutoff The date after which a model's training data ends; it knows little or nothing about later events unless given that information.

L

Basics Large language model (LLM) A neural network trained on very large amounts of text to predict the next token, which lets it write, answer questions, summarise, translate and code. Basics Latency How long a model takes to respond; often measured as time to first token.

M

Basics Machine learning A branch of AI in which systems learn to perform a task from examples and data rather than from explicitly written rules. Basics Multimodal Able to handle more than one kind of data, such as text, images, audio and video, in its input or output.

N

Basics Neural network A computing system made of layers of connected units whose connection strengths are learned from data.

O

Basics Open-weight model A model whose trained weights are published so anyone can download and run it, under the licence's terms.

P

Basics Parameter A number inside a model that is learned during training; a model's size is usually counted in parameters. Basics Prompt The input you give a model: instructions, questions, examples and any documents it should use.

S

Basics Small language model (SLM) A compact language model, often a few billion parameters, that is cheap and fast enough to run on a laptop, phone or modest server.

T

Basics Throughput How fast a model produces output, usually in tokens per second. Basics Token The unit of text a language model reads and writes: a word, part of a word or a punctuation mark. Usage is billed per token. Basics Tokenizer The component that splits text into tokens before a model processes it, and turns tokens back into text. Basics Training The process of adjusting a model's parameters on data so it learns to perform a task.

W

Basics Weights The learned numerical values of a neural network's connections; 'downloading a model' means downloading its weights.