Fine-Tuning

Fine-tuning updates some or all of a model's learned parameters using a targeted dataset. It can improve consistency, domain behavior, style, classification, or task performance, but it requires careful data quality, evaluation, and maintenance compared with prompt-only approaches. The concept matters when teams build production generative-AI applications that need reliable context, controlled outputs, evaluation, and connections to business data.