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.
Keeping knowledge fresh
RAG
AdvantageEasy — update the data, no retraining
Fine-tuning
Trade-offHard — needs retraining to add facts
Source citations & auditability
RAG
AdvantageStrong — can cite retrieved sources
Fine-tuning
Trade-offWeak — knowledge is opaque
Teaching tone, format or skills
RAG
Trade-offLimited — mostly adds facts
Fine-tuning
AdvantageStrong — shapes style and behaviour
Upfront cost & effort
RAG
AdvantageLower — index your content
Fine-tuning
Trade-offHigher — curated training data + compute
Per-query latency & cost
RAG
Trade-offSlightly higher (retrieval step)
Fine-tuning
AdvantageLean — no retrieval needed
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: August 2026
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