Retrieval Augmented Generation (RAG)
RAG Introduction
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Retrieval Augmented Generation (RAG) combines the knowledge access capabilities of information retrieval systems with the natural language understanding and generation abilities of large language models. RAG creates an architecture that can access, process, and incorporate information from diverse external sources—including databases, documents, APIs, and structured knowledge—before generating responses, creating more accurate, up-to-date, and verifiable AI outputs.
At its core, RAG addresses the fundamental limitations of traditional LLMs: their knowledge is frozen at training time, they lack source citations, and they're prone to hallucinations (confidently stating incorrect information). By grounding responses in retrieved contextual information, RAG significantly reduces these issues while maintaining the fluent, contextual understanding that makes LLMs so powerful. This approach enables AI systems to reason over private data, specialized domain knowledge, and real-time information that wasn't part of their original training.