What is RAG and why is it preferred over fine-tuning for domain-specific knowledge in production applications?

RAG (Retrieval-Augmented Generation) is a technique that finds relevant information from a knowledge base and injects it into the prompt before sending it to the LLM, effectively grounding the model's responses in real-time data[reference:0]. Its core value lies in solving the problem of outdated model knowledge and reducing hallucinations, making it ideal for dynamic scenarios[reference:1]. In contrast, fine-tuning updates the internal weights of the model, which is costlier and less flexible for rapidly changing information. For domain-specific knowledge in production, RAG is often preferred because it offers lower deployment costs, higher flexibility, and the ability to easily update the knowledge base without retraining the model. Experts note that the choice between RAG and fine-tuning can significantly impact costs, with RAG often being the more economical and agile choice for enterprise LLM applications[reference:2].