AI glossary

Which words in a discussion about artificial intelligence are doing real work, and which are doing marketing?

This glossary defines the terms that recur across demystifAI in plain language, with an emphasis on what each one means in practice for a financial institution. It is a living page, and it will grow as the writing does.

Definitions

• Large language model (LLM) — a model trained to predict text, which allows it to summarise, draft, classify, and reason over language. It carries no inherent notion of truth, which is precisely why the controls placed around it matter.
• Agent — a model given tools, a goal, and the latitude to take a sequence of steps towards it. The distinction from a chatbot is consequential: an agent acts, and therefore requires governance.
• Agentic workflow — a process in which one or more agents carry defined stages of the work, with human approval at the points where a decision is costly or irreversible.
• Retrieval-augmented generation (RAG) — supplying a model with the relevant documents at the moment of the question, so that the answer is grounded in a source rather than in recollection.
• Hallucination — a fluent, confident, and incorrect output. The term flatters the failure. It is more usefully understood as the model doing exactly what it was built to do, in the absence of grounding.
• Fine-tuning — further training of an existing model on specific examples in order to adapt its behaviour. Frequently proposed, and frequently the wrong instrument where the underlying problem is one of retrieval.
• Context window — the volume of text a model can consider at once. It bounds what the model is able to attend to, and it is a design constraint rather than a technical footnote.
• Prompt — the instruction and the context supplied to a model. In a production system, a prompt is an artefact under version control, not a sentence typed into a box.
• Evaluation — a repeatable test of whether a model or an agent produces acceptable output on the cases that matter. Without evaluations, a claim of improvement is an anecdote.
• Model drift — the degradation of performance as the world moves away from the conditions the model was built against. It is an operational risk, and it requires monitoring.
• Human in the loop — a design in which a person reviews or approves the output of an agent before it takes effect. The phrase is invoked far more often than it is specified. The useful questions are which step, and with what authority.
• Master data — the reference and entity data upon which downstream processes depend. In AI programmes it is usually the binding constraint, and it is usually discovered late.

Suggestions for terms to add are welcome.