The article explores decision factors for choosing between RAG and fine‑tuning LLMs in production, weighing model quality, cost, latency, and operational complexity. It offers a practical walkthrough of fine‑tuning methods including supervised fine‑tuning (SFT), LoRA, QLoRA, domain‑adaptive pretraining (DAPT), and direct preference optimization (DPO).