When building production AI systems, choosing between Retrieval-Augmented Generation (RAG) and fine-tuning can make or break your project. Here's a comprehensive analysis based on real-world deployments.
Performance Metrics
We tested both approaches across three different use cases: customer support, technical documentation, and code generation. Here are the key findings:
RAG Performance
- Response accuracy: 87% (with high-quality retrieval)
- Average latency: 2.3 seconds
- Setup time: 2-3 days
- Monthly cost: $450 (including vector DB)
Fine-tuning Performance
- Response accuracy: 94% (domain-specific)
- Average latency: 0.8 seconds
- Setup time: 1-2 weeks
- Monthly cost: $280 (inference only)
When to Choose RAG
RAG excels when you need:
- Rapid deployment and iteration
- Dynamic knowledge that changes frequently
- Transparency in information sources
- Lower upfront investment
When to Choose Fine-tuning
Fine-tuning is better for:
- Consistent, domain-specific tasks
- Lower latency requirements
- Stable knowledge domains
- Higher accuracy requirements
Hybrid Approach
In practice, the most successful deployments often combine both approaches: fine-tuning for core domain knowledge and RAG for dynamic, contextual information. This hybrid strategy delivers the best of both worlds while managing complexity and costs effectively.