Supervised Fine-Tuning

Supervised Fine-Tuning (SFT) trains pre-trained language models on curated prompt-response pairs to optimize instruction adherence, domain terminology, and structured output formatting. In Agent systems, SFT is commonly employed to distill complex intent classification, data extraction, and tool-calling protocols into cost-effective small models.