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Decision
Guide
Fine-tuning vs RAG
The most common question in applied LLMs: when to train the model vs. when to provide context at runtime?
TL;DR Decision Guide
Use RAG when:
- ✓ Data changes frequently
- ✓ Need to cite sources
- ✓ Factual accuracy is critical
- ✓ Quick deployment needed
- ✓ Multiple data sources
Use Fine-tuning when:
- ✓ Teaching new behaviors/style
- ✓ Domain-specific jargon
- ✓ Consistent output format
- ✓ Latency is critical
- ✓ Reducing inference costs
Detailed Comparison
| Aspect | RAG | Fine-tuning |
|---|---|---|
| Knowledge Updates | Instant (update vector DB) | Requires retraining |
| Hallucinations | Reduced (grounded) | Still possible |
| Latency | +retrieval overhead | Direct generation |
| Token Cost | Higher (context) | Lower per request |
| Setup Complexity | Vector DB + ingestion | Training pipeline |
| Data Required | Documents (unstructured OK) | Labeled examples (structured) |
| Best For | Knowledge, facts, Q&A | Behavior, style, format |
Use Case Examples
RAG Examples
- • Customer support with product docs
- • Legal research over case law
- • Enterprise search over internal wikis
- • Q&A over financial reports
Fine-tuning Examples
- • Consistent JSON output format
- • Brand voice/tone adaptation
- • Medical terminology understanding
- • Code generation for specific frameworks
RAG + Fine-tuning (Best of Both)
- • Fine-tune for domain jargon + RAG for current data
- • Fine-tune for output format + RAG for facts
- • Distill retrieval behavior into smaller model
Decision Flowchart
1. Does the model need to know facts it wasn't trained on?
→ Yes → RAG
2. Does the data change frequently (daily/weekly)?
→ Yes → RAG
3. Do you need to cite sources?
→ Yes → RAG
4. Do you need to change the model's behavior/style?
→ Yes → Fine-tuning
5. Is consistent output format critical?
→ Yes → Fine-tuning
6. Is inference latency critical?
→ Yes → Fine-tuning (or both)