Retrieval-Augmented Generation (RAG)

Retrieval-augmented generation, or RAG, enables a large language model to retrieve relevant information from a designated knowledge base before generating a response, improving timeliness, traceability and suitability for specialized domains. Recent applications have expanded from law and education to cybersecurity and enterprise AI agents, while developers continue to refine real-time retrieval, long-term memory, knowledge precompilation and chunking methods. At the same time, retrieval poisoning, insufficient sourcing and evaluation debt continue to create deployment risks. RAG has become a key architecture for connecting internal enterprise data to generative AI, making its accuracy, cost and governance capabilities important areas to monitor.