Fine-Tune vs RAG

Fine-tuning and retrieval solve different problems, and the choice is usually decided by whether your requirement is about behaviour or about facts. Fine-tuning adjusts the model's weights on your examples. It is the right tool for形 form and style — a consistent output structure, a house tone, a classification scheme with fuzzy boundaries, a domain vocabulary the base model garbles. What it is bad at is knowledge that changes: retrain every time a price list moves and you have built a very expensive database. Retrieval leaves the model alone and puts the relevant text in front of it at request time. It handles freshness, per-customer data and citation — the answer can point at the document it came from, which fine-tuning cannot do. Most teams asking for a fine-tune describe a retrieval problem: "it doesn't know our products" is a facts issue. The order that works is prompt, then retrieval, then fine-tune only when the failure that remains is stylistic or structural rather than factual — and by then you have an evaluation set from the first two stages to prove the third was worth it.