RAG vs Fine-tuning
A constant question: should you retrieve context at query time (RAG) or bake knowledge into the model (fine-tuning)? They solve different problems and are often combined.
At a glance
RAG
- Best for
- Injecting fresh, factual knowledge
- Freshness
- Update the index, no retraining
- Cost to change
- Cheap — edit the data
Fine-tuning
- Best for
- Teaching format, style, or narrow behavior
- Freshness
- Requires retraining to update
- Cost to change
- Expensive — re-run training
Full comparison
| RAG | Fine-tuning | |
|---|---|---|
| Best for | Injecting fresh, factual knowledge | Teaching format, style, or narrow behavior |
| Freshness | Update the index, no retraining | Requires retraining to update |
| Cost to change | Cheap — edit the data | Expensive — re-run training |
| Hallucination control | Strong with good retrieval + citations | Weaker; model still generalizes |
| Setup | Embeddings + retrieval pipeline | Training data + training run |
Which should you choose?
Default to RAG for knowledge that changes or must be cited; reach for fine-tuning to lock in a consistent format, tone, or a narrow skill. The strongest systems use both — RAG for facts, light fine-tuning for behavior.
Frequently asked questions
What's the difference between RAG and Fine-tuning?
A constant question: should you retrieve context at query time (RAG) or bake knowledge into the model (fine-tuning)? They solve different problems and are often combined.
Which should I choose, RAG or Fine-tuning?
Default to RAG for knowledge that changes or must be cited; reach for fine-tuning to lock in a consistent format, tone, or a narrow skill. The strongest systems use both — RAG for facts, light fine-tuning for behavior.