RAG vs Fine-Tuning: When to Use Each

RAG systems convert documents into vector embeddings and store them in a vector database. At query time, the user question is embedded, similar documents are retrieved via vector search, and retrieved context is included in the LLM prompt. The LLM generates a response grounded in the retrieved information.

This approach keeps knowledge current without retraining, enables source attribution, and works with any foundation model. You can update your knowledge base by simply adding new documents—no model retraining required.

RAG excels in several scenarios:

RAG can be implemented in days versus weeks for fine-tuning, making it ideal for rapid prototyping.

Fine-tuning is the better choice when:

Fine-tuning teaches the model new behaviors at a fundamental level.

Production systems often combine both approaches for best results:

For example, a legal AI might be fine-tuned on legal writing style while using RAG to retrieve relevant case law. A customer support bot could be fine-tuned on your brand voice while using RAG to access product documentation.

This separation of concerns—style from facts—provides the best of both worlds: consistent behavior with accurate, up-to-date information.