Fine-tuning continues training from an existing model rather than starting from random parameters. The added examples and objective adjust the stored weights to emphasize desired capabilities, formats, terminology, or behavior.
The process creates a lasting model change, unlike in-context learning, which adapts behavior through the current input. Fine-tuning needs suitable data and evaluation because it can also introduce regressions or overfit narrow examples.
ELI5
Fine-tuning continues training an existing AI model on additional examples so its stored weights better fit a task, field or preferred behavior. It starts from a model that already knows broad patterns instead of learning everything from zero.
For example, a company can fine-tune a model on reviewed examples of its support format. The change remains in the new model version, so teams need evaluations for improvement, regressions and overfitting rather than assuming more training always helps.
