Fine-Tuning vs RAG: An In-Depth Comparison

Fine-tuning modifies a model's weights by training it on your specific data, permanently changing its behavior. RAG (Retrieval-Augmented Generation) keeps the base model unchanged and retrieves relevant documents at query time to provide context.

Use RAG when your data changes frequently, you need source attribution, or you want to avoid retraining costs. Use fine-tuning when you need consistent style or tone, domain-specific behavior patterns, or lower inference latency. Most production systems benefit from combining both.

Yes, and this is often the best approach. Fine-tune a model for your domain's style and terminology, then use RAG to provide current data and specific documents at query time.