This episode addresses how we turn a raw base model into something that behaves like a real assistant using Supervised Fine-Tuning (SFT). We explore instruction and response training data, why SFT makes behaviors consistent beyond prompting, and the practical engineering choices that keep fine-tuning efficient and safe, including low learning rates and LoRA-style adapters. By the end, you will understand what SFT solves, and why the next layer (RLHF) is needed to add human preference and nuance.