When would you fine-tune an LLM versus use RAG?

Use RAG when answers must be grounded in factual, changing, or private data — you update documents, not weights, and get citations. Use fine-tuning to teach a consistent style, format, or narrow behaviour the base model can't reliably follow through prompting. RAG is usually cheaper to keep current; fine-tuning needs curated data and re-runs when things change. They combine well: RAG for knowledge, light fine-tuning for tone. Reach for prompting first — it solves more than people expect.