RAG and fine-tuning solve different AI app problems
Production AI apps should first check whether the model lacks the right information before moving to . RAG fits knowledge-heavy work such as internal documents, company search, customer support , product , and company policies. A team can update the to add new information instead of retraining the model.
Answers can also point back to source documents, which makes them easier to verify than relying on model memory alone. Testing can move faster because teams can adjust the , , or reranker instead of retraining a large LLM. is better when the main issue is the model’s behavior, , or output format.
In real s, RAG and often work together rather than replacing each other.
Key points
- Use RAG first when the missing piece is current or private knowledge.
- Use when the model needs a steadier style, behavior, or output shape.
- RAG can reduce retraining work because the can be updated directly.
- Source links make answers easier to check.
- Many production setups combine RAG and .