RAG Explained: How AI Can Answer From Your Documents Without Retraining

Retrieval-Augmented Generation (RAG) is an AI architecture that connects large language models to private or up-to-date document collections without requiring the model to be retrained. Standard LLMs rely on knowledge frozen at training time, making them prone to outdated or hallucinated answers when queried about business-specific data. Fine-tuning was the early alternative, but it proved slow, costly, and unable to keep pace with frequently changing documents. RAG solves this by retrieving relevant document excerpts at query time and feeding them directly into the model's prompt, so the model reads current information rather than recalling stale parameters. First proposed in a 2020 Facebook AI Research paper by Lewis et al., the approach has since become one of the most widely adopted patterns in applied AI.