Retrieval-Augmented Generation (RAG) is a technique that enhances large language models by grounding their responses in factual information retrieved from a knowledge base. Rather than relying solely on the model's training data, RAG systems fetch relevant documents or data sources and use them to inform the AI's answer, significantly improving accuracy and relevance.
RAG works by converting user queries into semantic embeddings, searching a vector database for similar content, and providing those retrieved documents as context to the language model. This approach solves a critical problem: LLMs can hallucinate or provide outdated information when forced to rely purely on their training data.