RAG vs Fine Tuning
RAG grounds answers in retrieved enterprise content at query time. Fine tuning adapts model behavior from training data. Enterprises often need RAG for facts and may use fine tuning for style or specialized recognition.
Quick Summary
RAG grounds answers in retrieved enterprise content at query time. Fine tuning adapts model behavior from training data. Enterprises often need RAG for facts and may use fine tuning for style or specialized recognition.
Definition: RAG
Retrieval-augmented generation — fetch evidence, then generate with citations.
Definition: Fine Tuning
Further training a model on domain examples to change behavior or recognition.
Comparison table
| Dimension | RAG | Fine Tuning |
|---|---|---|
| Fact freshness | Updates when sources update | Stale until retrained |
| Access control | Filter at retrieval time | Harder to enforce per-document ACLs in weights |
| Citations | Natural fit | Not inherent |
| Cost pattern | Retrieval + inference | Training + inference |
| Best fit | Policies, manuals, tickets, records | Tone, classification heads, domain jargon |
Business use cases
RAG
- Policy copilots
- Grounded customer support answers
- Procedure lookup
Fine Tuning
- Domain classification models
- Consistent drafting style
- Specialized entity recognition
Decision guidance
Default to RAG for enterprise factual answers over changing corpora. Consider fine tuning when you need specialized behavior that retrieval cannot provide. They can be combined.
Key Takeaways
- RAG: Retrieval-augmented generation — fetch evidence, then generate with citations.
- Fine Tuning: Further training a model on domain examples to change behavior or recognition.
- Decision: Default to RAG for enterprise factual answers over changing corpora. Consider fine tuning when you need specialized behavior that retrieval cannot provide. They can be combined.
Related: Glossary: Rag · Glossary: Fine Tuning · Insight · Insight · All comparisons · Glossary