Comparison
RAG vs fine-tuning: which should you use?
Both make an LLM smarter about your domain, but they solve different problems. RAG retrieves knowledge at query time; fine-tuning bakes behaviour into the model.
| Dimension | RAG | Fine-tuning |
|---|---|---|
| Keeping knowledge fresh | Easy — update the data, no retraining | Hard — needs retraining to add facts |
| Source citations & auditability | Strong — can cite retrieved sources | Weak — knowledge is opaque |
| Teaching tone, format or skills | Limited — mostly adds facts | Strong — shapes style and behaviour |
| Upfront cost & effort | Lower — index your content | Higher — curated training data + compute |
| Per-query latency & cost | Slightly higher (retrieval step) | Lean — no retrieval needed |
Our verdict
Start with RAG — it's cheaper, more transparent and easy to keep current. Reach for fine-tuning when you need a specific tone, format or task skill that retrieval can't teach. The strongest systems often use both.
Discuss your case with usLast updated: June 2026
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