How AI is built

The text, images and code a model learnt from: much of the public web, books, licensed sources, and sometimes users' conversations.

The long first stage of building a model, where it learns general patterns from an enormous body of material before anyone teaches it to be helpful.

Taking a trained model and training it further on a smaller, specific set of examples: your firm's writing style, a legal domain, a support script.

Reusing what a model learnt on one large task as the starting point for a different, usually smaller one, instead of starting from nothing.

Training by trial and reward rather than by copying examples: the system tries something, gets scored, and shifts towards what scored well.

The stage where people rank a model's competing answers and it is trained towards the ones they preferred, which is much of why assistants feel helpful.

Training a smaller model to imitate a larger one, to get most of the quality at a fraction of the cost and wait.

Storing a model's numbers at lower precision so it needs less memory and can run on smaller hardware, giving up a little accuracy.

Training material generated rather than collected, used where real examples are scarce, sensitive or expensive to label.

The kind of chip AI runs on, built to do enormous numbers of simple calculations at once, which is exactly the shape of the work models do.

Marking up examples so a model can learn from them, such as tagging which emails are complaints, usually slow and done by people.

A model adapted to one organisation's data or tone, which vendors sell as a premium tier and which needs upkeep as the underlying model moves.

Naming and dating the exact model behind a feature, so results can be reproduced and a change can be traced to a cause.

A vendor retiring a model version on a published date, after which anything built on it must move or stop working.

A vendor's published account of how a whole AI system behaves, including its known weaknesses and the testing it was put through.

A short standard summary of one model's intended uses, training approach and limitations, published so buyers can compare like with like.

Everything done to shape a model after its main training run, which is where most of its manners and refusals come from.

The date a model's training data stops, after which it knows nothing unless the information is searched for or supplied in the request.