Fine-Tuning vs RAG: Which Should You Use?
Use RAG when knowledge changes often or must be cited; use fine-tuning to shape style and behaviour. Many production systems use both together.
Quick version: RAG for fresh, citable knowledge; fine-tuning for consistent style and behaviour; often both.
| Approach | Best for | Keeps knowledge current? |
|---|---|---|
| RAG | Changing, citable knowledge | Yes — update the documents |
| Fine-tuning | Style, format, behaviour | No — retrain to change |
Start with RAG for most knowledge problems; reach for fine-tuning when you need the model to consistently respond a certain way. The strongest systems frequently use both — a core decision in applied AI.
Frequently asked questions
Should I use fine-tuning or RAG?
Use RAG when your knowledge changes frequently or answers must be cited, since it updates as your documents update. Use fine-tuning to shape consistent style, format or behaviour. Many systems combine both.
Can you combine fine-tuning and RAG?
Yes — and it is common. Fine-tuning shapes how the model responds while RAG supplies current, source-backed knowledge, giving you both consistency and freshness.
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