Modeling ยท 10 min read
Fine Tuning vs RAG: Choosing the Right Lever
When to fine tune, when to retrieve, and how to balance cost, latency, and compliance.
The core decision
Fine tuning changes model behavior. RAG changes model context. You need to pick the lever that matches your problem. If the model needs new knowledge that changes often, choose RAG. If the model needs consistent behavior and tone, fine tuning can help.
Choose RAG when
- Knowledge updates frequently
- You need traceable sources
- You must control access per user or team
- You want fast iteration without retraining
Choose fine tuning when
- You need consistent output format and tone
- You have a stable, high quality dataset
- Latency must be minimal and context is limited
- You need specialized behavior across many tasks
Most teams need both
In practice, a tuned model plus a RAG layer is the most reliable approach. The tuned model handles style and behavior, while the RAG layer supplies fresh knowledge.
Cost and compliance
RAG adds storage and retrieval cost. Fine tuning adds training cost and evaluation overhead. For regulated industries, RAG can be safer because you can audit sources. For high volume tasks, fine tuning can reduce inference cost.