The Promise of RAG: Bringing Enterprise Generative AI to Life

Retrieval-Augmented Generation (RAG) enhances large language models by integrating them with an organization's specific, up-to-date knowledge base. The process combines two steps: retrieval, which searches documents for relevant information, and augmented generation, which uses that retrieved context to create a grounded, accurate response. This technique improves factual accuracy, minimizes risk, and allows for more specific and personalized text generation. Common enterprise use cases include internal productivity tools, analysis assistants, and external-facing customer support chatbots.