What does RAG retrieval-augmented generation mean?

RAG (Retrieval-Augmented Generation) is a technique that enhances large language models (LLMs) by integrating external knowledge sources into the generation process. It allows AI systems to produce more accurate, relevant, and up-to-date responses grounded in specific retrieved information.

RAG operates by first retrieving relevant documents or passages from a designated knowledge base (like a database or document store) based on the user's query. The retrieved information and the query are then fed into the LLM simultaneously. This context conditions the LLM's generation process, significantly improving answer factuality and reducing hallucinations compared to relying solely on the LLM's internal training data. Key considerations include the quality and coverage of the knowledge base, the effectiveness of the retrieval mechanism, and balancing relevance with novelty.

RAG is crucial for applications requiring accurate, verifiable answers or needing to utilize proprietary, domain-specific, or frequently updated information that isn't fully captured in an LLM's pre-training. It significantly enhances chatbots, virtual assistants, research tools, and enterprise knowledge systems, ensuring responses are not just plausible but reliably sourced. This approach adds verifiable grounding and trustworthiness to generative AI interactions.