Retrieval-Augmented Generation (RAG) has served as the foundational pillar for the initial wave of enterprise generative AI adoption, offering a pragmatic solution to the inherent hallucinations of large language models (LLMs). By grounding model outputs in proprietary documentation, RAG allowed organizations to leverage internal data for customer support, technical documentation, and internal search....
