Fine-Tuning

Adapting a pretrained model through additional training on examples of a desired task, domain, or behavior. It can improve format, style, classification, and recurrent decisions when those patterns are consistent in the data and the evaluation. It does not inject an updatable knowledge base; facts that change frequently remain a context, retrieval, or tool problem.

Fine-tuning inherits the limits and biases of its dataset. Duplicated examples, inconsistent labels, sensitive data, or a distribution that does not represent real use become part of the model's behavior. The decision should compare the gain with less permanent alternatives, such as structured prompting, RAG, or deterministic validation, and measure regressions before replacing a baseline.