Fine-Tuning vs Prompting

Prompting teaches at inference time: instructions, examples, and context go into the window, and nothing about the model itself changes. Instant, cheap, reversible — test a new instruction in seconds.

Fine-tuning teaches at training time: show the model hundreds-to-thousands of ideal input/output pairs and update its weights, so the behavior becomes the default rather than a request.