Fine-tuning adjusts model weights using curated input-output examples. It is best suited for style consistency, classification, and tasks requiring very specific output formats. For most business use cases, RAG and sophisticated prompting deliver better ROI than fine-tuning, which requires data curation, compute, and ongoing maintenance as base models improve.