AI glossary

AI terms explained clearly

Concise definitions, technical background and links to related guides, topics and services.

A

Agentic AI

Agentic AI describes AI systems that independently plan multi-step tasks, call tools, and evaluate intermediate results, instead of answering a single request.

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AI Gateway

An AI gateway is a central layer between applications and AI models that bundles access, logs requests, enforces security rules, and can route between different providers.

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C

Context Window

The context window is the maximum amount of text, measured in tokens, that a language model can process at once in a single request.

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E

Embedding

An embedding represents text, images or other data as a numeric vector. Semantically similar data receives similar vectors.

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F

Fine-Tuning

Fine-tuning further trains an already trained model on additional, specific data to specialise it for a particular task or style.

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G

Guardrails

Guardrails are technical and organisational safeguards that define what an AI system may do, which data it may see and when human review or approval is required.

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H

Hallucination

A hallucination is a plausibly phrased but factually incorrect or fabricated output from an AI model.

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I

Inference

Inference is the process where an already trained AI model processes an input and produces an output, as opposed to training, where the model itself is adjusted.

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L

Large Language Model (LLM)

A large language model is an AI model trained on large amounts of text that processes language patterns to generate, summarise and edit text.

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M

MCP (Model Context Protocol)

MCP is an open standard that lets AI models access external tools, data, and systems in a uniform way, instead of building a custom integration for every connection.

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P

Prompt Engineering

Prompt engineering is the systematic design of instructions given to a language model, aimed at getting more reliable, precise, or better structured answers.

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R

RAG (Retrieval-Augmented Generation)

RAG combines a language model with a search over your own documents, so answers are grounded in current, company-specific knowledge instead of training data alone.

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T

Token

A token is a processing unit for text, often a word, word fragment or punctuation mark. Tokens affect cost, speed and context size.

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Tool Use (Function Calling)

Tool use is a language model's ability to call external functions in a structured way while forming an answer, for example a search, a calculation, or a database query.

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V

Vector Database

A vector database stores embeddings and retrieves entries according to their mathematically determined similarity.

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AI terms explained clearly

A

Agentic AI

AI Gateway

C

Context Window

E

Embedding

F

Fine-Tuning

G

Guardrails

H

Hallucination

I

Inference

L

Large Language Model (LLM)

M

MCP (Model Context Protocol)

P

Prompt Engineering

R

RAG (Retrieval-Augmented Generation)

T

Token

Tool Use (Function Calling)

V

Vector Database

What would you like to assess or build?

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