When to Choose Each Option
Clear guidance based on your specific situation and needs.
Our Recommendation
RAG is the better default choice for most enterprise use cases — it's cheaper, more flexible, and keeps knowledge up-to-date without retraining. Fine-tuning excels when you need to change the model's behavior, style, or reasoning patterns, or when latency is critical. Many production systems combine both approaches.
- Choose Fine-Tuning when...
- Need cost-effective solutions for updates.
- Require flexibility in knowledge management.
- Focus on enterprise-level applications.
- Choose RAG when...
- Need to change behavior in AI systems.
- Require specific customization for tasks.
- Combine methods for optimal results.