Fine-tuning vs RAG

When you need a model to know something new, you have two fundamentally different levers:

Fine-tuning updates the model's weights using additional supervised or preference data. Training can change behavior and learned associations, but it does not reliably install a factual database. Serving cost depends on the model, adapters, and provider.

• ✅ Best for: style, tone, format, tool-use patterns, reasoning style.
• ❌ Worst for: fresh facts — every update requires another training run, and old knowledge can be forgotten (catastrophic forgetting).
• Cost: data labeling + GPU hours.

Retrieval-Augmented Generation (RAG) keeps the base model unchanged and instead provides external context at query time. A retriever finds relevant chunks from an index, and the model conditions on them.

• ✅ Best for: fresh, citable, fast-changing facts — docs, policies, internal wikis.
• ❌ Worst for: teaching a new reasoning style — the model already knows how to use the context, but RAG alone does not change how it reasons.
• Cost: an index and per-query retrieval latency.

Use them together. Fine-tune for behavior, RAG for knowledge. Whether both are needed depends on evaluation results and the application.