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Learn · Fine-tuning vs RAG

Fine-tuning vs RAG

The most common question leaders ask about customising an LLM. They solve different problems: RAG changes what the model knows; fine-tuning changes how it behaves. Toggle your requirements and see what fits.

Your requirements — tap all that apply:

Recommendation:

Start with prompt engineering

No special requirements yet — the cheapest, fastest option is a good prompt. Add requirements to see when RAG or fine-tuning earns its place.

RAG signals: 0 Fine-tune signals: 0
Dimension
RAG
Fine-tuning
Changes
What the model knows
How the model behaves
Fresh data
Easy — update the index
Hard — needs retraining
Citations
Yes, per source
No, baked into weights
Upfront cost
Low
Higher (training + data)
Per-query cost
Higher (retrieval)
Lower once trained
Best for
Knowledge & grounding
Style, format, niche skills

The short version

Reach for RAG when the problem is knowledge — current facts, private documents, answers that must cite sources. Reach for fine-tuning when the problem is behaviour — a consistent tone, a strict output format, or a narrow skill the base model fumbles. Many production systems use both: fine-tune the style, retrieve the facts. And always try a good prompt first. See how retrieval works in the RAG demo.