What are RAG models? A guide to enterprise AI in 2025

This post explains the concept of Retrieval-Augmented Generation (RAG) models, detailing their architecture (indexing, retrieval, augmentation, generation) and benefits (timeliness, transparency, efficiency, modularity, scalability). It highlights RAG's importance for enterprise AI by addressing information fragmentation and increasing trust. Use cases in search, customer support, sales enablement, and content generation are discussed. Limitations related to data quality and semantic reasoning are acknowledged, emphasizing the shift towards grounded, adaptive AI. Glean's application of RAG for real-time search and assistive experiences is mentioned.