What RAG Is and When It Helps Language Models Answer From Private Data

Retrieval-augmented generation, or RAG, lets a model search relevant documents from a user's own data before responding rather than relying solely on its training. The approach is likened to an open-book exam, since it supplies current or private information without retraining the model. It is especially useful for questions about internal documents, tickets, or policies, and it can reduce hallucinations by grounding answers in source text and enabling citations. The article cautions that RAG adds latency and complexity for tasks like pure reasoning, creative writing, or code generation that involve no private data. Common pitfalls include poor document chunking, retrieving irrelevant passages, and failing to measure retrieval quality rigorously.