Retrieval Augmented Generation (RAG) – The key to enabling generative AI for the enterprise

This post explains the core principles of Retrieval Augmented Generation (RAG) as a framework for enabling generative AI in the enterprise. It details how RAG separates knowledge retrieval from the generation process, grounding LLM responses in real, external enterprise knowledge. Key aspects highlighted include the importance of relevancy, recency, and permissions in the retrieval phase, and how this approach mitigates hallucinations and provides traceable, referenceable answers. The post contrasts RAG with fine-tuning, emphasizing RAG's cost-efficiency and ability to handle dynamic information.