How AI technology fits together

Four layers, one idea each, no jargon you have to take on trust.

01Data: what the system learns from

Every AI system starts with examples: text, images, sound, tables. The quality and range of the examples set the limits of what comes out. A system that has never seen a kind of input has no reason to handle it well.

02Models: what gets learned

A model is a large set of numbers, tuned during training so that its outputs match the examples. It stores patterns, not a copy of each example. Training happens once, or in occasional rounds; using the model afterwards is a separate step.

Training and using a model are different jobs with different costs. Most confusion in the news comes from mixing them up.

03Compute: the machines that do the arithmetic

Training and running a model are arithmetic on a very large scale, done on chips built for it, in data centers. Compute is why some tasks answer in a blink and others take a minute: more work per answer, more time.

04Applications: what people actually touch

A chat window, a photo tool, a search box, a spreadsheet helper. The application hides the layers below it and adds the parts a person needs: an interface, saved history, limits, safety checks. Two apps can sit on similar models and feel entirely different.

++Words to know

Training: Tuning a model on examples until its outputs fit them.
Inference: Using a trained model to answer one request.
Parameter: One of the numbers that make up a model.
Prompt: The input a person gives a model to respond to.

This is a concept site. If aiprimetech.com is yours, the guide's design and files can come with it. Ask or make an offer.