RAGFine-TuningMachine LearningDomain Adaptation
RAG vs Fine-Tuning: When to Use Each Approach
Compare Retrieval-Augmented Generation with model fine-tuning for domain-specific applications. Learn which approach suits your use case.
Understanding RAG
RAG retrieves relevant documents at query time and includes them in the prompt. Best for dynamic knowledge that changes frequently.
When to Fine-Tune
Fine-tuning adjusts model weights for specific patterns. Best when you have stable, domain-specific terminology and consistent response styles.
Decision Matrix
| Factor | Choose RAG | Choose Fine-Tuning |
|---|---|---|
| Knowledge updates | Daily/Weekly | Rarely |
| Training data | Few examples | Thousands |
| Response consistency | Flexible | Fixed style |
| Latency tolerance | Moderate | Low |
Hybrid Approach
Use RAG for current knowledge and fine-tuning for style adaptation. This combines the best of both approaches.