AI Glossary

Governance & Regulation

EU AI Act

Regulation (EU) 2024/1689 entered into force on 1 August 2024 and applies in phases: prohibited practices from February 2025, general-purpose AI model obligations from August 2025, and the bulk of the framework from August 2026. It sorts systems into unacceptable, high, limited, and minimal risk, with obligations scaling accordingly. It reaches non-EU providers whose output is used in the EU. In practice: A US SaaS company selling into the EU is in scope regardless of where it is incorporated. ...

Trust, Risk & Safety

Evaluation (Evals)

Evals are the automated test suite for probabilistic software: a fixed set of cases with expected outcomes, run on every change. They are the only way to know whether a prompt tweak or model upgrade helped or quietly broke something. Public benchmarks tell you about the model; evals tell you about your system. In practice: 200 real tickets with correct answers, run against every prompt change.

Trust, Risk & Safety

Explainability

Explainability is about the audience: an explanation must be usable by the person who needs it — an applicant, an auditor, an engineer. It is often achieved with post-hoc methods that approximate the model’s behaviour rather than reveal its actual mechanism. That approximation is a real limitation, not a technicality. In practice: ‘Declined mainly due to debt-to-income ratio and short credit history.’

Trust, Risk & Safety

Fairness

Fairness sounds like one property but is many: equal accuracy across groups, equal false-positive rates, equal outcomes. It is mathematically proven that you cannot satisfy all reasonable definitions at once except in trivial cases. So fairness work means choosing which definition your context demands and defending that choice. In practice: Equalising error rates across groups can force unequal approval rates, and vice versa.

Foundations

Feature

A feature is a single measurable property of an example — a price, a pixel, a word count. Classical ML depended on humans engineering good features; deep learning largely learns them from raw input instead, which is a big part of why it took over. In practice: For house-price prediction: square metres, postcode, year built — each is a feature.

Prompting

Few-Shot Prompting

Few-shot prompting teaches by demonstration inside the context window, with no training involved. It is the highest-leverage prompting technique for consistency, especially on classification and formatting. Choose examples that cover the edge cases, not just the easy ones — the model will copy whatever you show it, including the gaps. In practice: Five labelled tickets in the prompt, and category accuracy jumps without any fine-tuning.

Generative AI & LLMs

Fine-Tuning

Fine-tuning updates a pretrained model’s weights on a smaller, targeted dataset. It is the right tool for teaching form — tone, format, a classification scheme — and the wrong tool for teaching facts, which RAG handles better and cheaper. Most teams try fine-tuning too early; prompt work and retrieval solve more problems than expected. In practice: 500 examples of your support tone, so replies stop sounding like a press release. ...

Generative AI & LLMs

Foundation Model

The foundation model idea inverts the old workflow: rather than training a fresh model for every problem, you train one general model at great expense and adapt it cheaply via prompting or fine-tuning. That economics is why a handful of labs train models and everyone else builds on top. The EU AI Act addresses much the same thing under the name general-purpose AI model. In practice: One base model powering a support bot, a code assistant, and a summariser — all with the same weights. ...

Generative AI & LLMs

Frontier Model

Frontier model has no crisp technical threshold; it means whatever is currently most capable and therefore least well understood. The term matters mainly in policy, where extra evaluation and reporting obligations attach to the leading edge. The EU AI Act’s nearest concept is a general-purpose AI model with systemic risk. In practice: Every generation, yesterday’s frontier model becomes today’s ordinary default.

AI Agents & Automation

Function Calling

You describe your functions and their parameters; the model returns a structured request naming one and supplying arguments. Critically, the model does not execute anything — your application does, which is where validation and authorisation belong. It is the plumbing under almost every agent product. In practice: The model emits get_order_status(order_id=‘A-4471’); your backend decides whether to run it.