Fine-Tuning versus RAG: A Decision Framework

They are usually presented as alternatives when they change different things: fine-tuning adjusts behaviour, retrieval supplies knowledge. This cursus makes the choice properly. Why fine-tuning is a poor way to add facts, what it is genuinely good at, and the three questions that settle the matter on their own. Then what building each involves, from LoRA and the training-data problem that stalls most projects to the retrieval pipeline. Then the options the framing hides, long context, prompt caching, agentic retrieval, and how to revisit a decision that has expired.