The process that sets a model's parameters, by exposing it to vast amounts of text and adjusting parameters to improve next-token prediction. A one-time, expensive process done by the model provider. Encompasses both pre-training (the bulk run) and post-training (later refinements like instruction-following and safety); the distinction doesn't matter at this glossary's level.
The mechanism is repetition at scale: show the model a stretch of text, have it predict the next token, nudge the parameters toward whatever the actual next token was, and repeat across trillions of tokens. Nothing is stored as facts or rules — everything the model "knows" is a side effect of getting better at prediction, compressed into the parameters as parametric knowledge.
Two consequences matter day to day. Training ends at a point in time, so the model has a knowledge cutoff — it hasn't seen the library version you upgraded to last month. And training is not something you can do: when the model doesn't know your codebase, your conventions, or your internal APIs, the fix is never "teach the model" — it's putting that material into context, the one input you control.
Usage:
"Can we get it to know our internal API?"
"Not via training — that's a months-long process by the model provider. Load the API docs into context instead, that's the lever you actually have."