AI Model
An AI model is the finished artefact that comes out of training: a file of tuned numbers that maps inputs to predictions. You build it once and run it as many times as you like; running it is called inference.
No jargon, no math
In plain words, what is it?
Training is the factory, the model is the product. It is not code you can read line by line; it is a large set of parameters (weights) plus the recipe to load and run them. Feed an input and the numbers transform it into an output: text in, answer out; photo in, label out. One model does one job, the job it was trained on, and nothing else: a defect detector will not write emails no matter how politely you ask. Models are frozen while the world moves, so their accuracy decays (drift) until you retrain or replace them. The boundary: a model is only ever as good as the data it saw, and its confidence is a number, not evidence.
Terms that live nearby.
Training
Training is the process that turns an untrained model into a working AI: you feed it thousands or millions of examples, compare its guesses with the correct answers, and nudge its internal settings until the guesses get good.
ReadInference
Inference is using a trained model: you send input in, the model applies what training encoded in its parameters, and you get an answer out, one token at a time.
ReadParameter
A parameter is one of the internal numbers a trained model stores: billions of them, each tuned during training, and together they encode everything the model learned.
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