AI and Data Glossary

A working glossary of the AI and data concepts that matter in real institutions, defined in plain English. The terms are grouped into four sections: foundations, working with models, knowledge and retrieval, and safety, governance, and delivery. Each definition is kept short and practical, so the glossary explains not only what a concept means but why it matters when you build and govern AI systems.

Foundations

The core building blocks every other term depends on, from what a model is to the units and limits it works within.

  • Agent — A system that pursues a goal by repeatedly perceiving, reasoning, acting, and observing, rather than answering a single prompt.
  • Agentic AI — AI that takes autonomous, multi-step action toward a goal, using tools and feedback, under human oversight.
  • Large Language Model (LLM) — A model trained on large text corpora that generates language and serves as the reasoning engine in most agents.
  • Foundation model — A large, general-purpose model trained on broad data that can be adapted to many downstream tasks.
  • Transformer — The neural network architecture, based on attention, behind most modern language and multimodal models.
  • Token — The unit of text a model reads and generates; cost and context limits are measured in tokens.
  • Context window — The maximum amount of text, in tokens, a model can consider at once.
  • Parameters — The internal weights a model learns during training; model size is often quoted as a parameter count.

Working with Models

How you shape a model's behaviour and adapt it, from prompting to training methods.

  • Prompt engineering — Designing instructions and examples that produce reliable, checkable model behaviour.
  • Zero-shot / few-shot — Asking a model to perform a task with no examples, or with a handful of examples placed in the prompt.
  • Temperature — A setting that controls randomness in output; lower is more deterministic, higher is more varied.
  • Inference — Running a trained model to produce an output, as opposed to training it.
  • Fine-tuning — Further training a model on specific data to specialise its behaviour.
  • RLHF — Reinforcement learning from human feedback, a method that aligns model behaviour with human preferences.
  • Chain-of-thought — Prompting a model to reason step by step before it answers.
  • Multimodal AI — Models that work across more than one type of data, such as text, images, and audio.

Knowledge and Retrieval

How AI systems find, store, and act on information beyond what the model memorised in training.

  • Embedding — A numerical representation of text or data that captures meaning and enables semantic search.
  • Vector database — A store optimised for embeddings, used to retrieve semantically similar content quickly.
  • Retrieval-Augmented Generation (RAG) — Grounding an answer in retrieved source documents so the model cites facts instead of inventing them.
  • Tool use / function calling — Allowing a model to call external functions or systems to take real actions.
  • Model Context Protocol (MCP) — An open standard for connecting AI systems to tools and data sources in a consistent way.
  • Orchestration — Coordinating multiple models, tools, or agents into a reliable workflow.
  • Knowledge graph — A structured network of entities and relationships used to represent and query knowledge.
  • Semantic search — Finding results by meaning rather than exact keywords, powered by embeddings rather than literal text matching.

Safety, Governance and Delivery

The terms that decide whether an AI system is safe to run where the consequences are real.

  • Hallucination — A confident but false or unsupported output from a model.
  • Guardrails — Controls that constrain what a model or agent is allowed to do or say.
  • Evals — Structured tests that measure whether an AI system performs correctly and safely.
  • Human-in-the-loop — A design where a person reviews, approves, or overrides AI outputs at the points that matter.
  • Data governance — The policies and controls that manage how data is collected, protected, and used.
  • NIST AI RMF — The NIST AI Risk Management Framework, a voluntary structure to govern, map, measure, and manage AI risk.
  • PII — Personally identifiable information; data that can identify an individual and must be protected.
  • Last-Mile AI — A delivery approach for building AI systems that work under real-world infrastructure, language, governance, and operational constraints. Read the full Last-Mile AI overview.

To see these concepts in depth, the free Learn LLMs course teaches large language models and the Transformer from zero, with interactive labs.