Retrieval-augmented generation (RAG) for LLMs

This post describes the core concepts and benefits of Retrieval-Augmented Generation (RAG) for LLMs. It details the RAG workflow, including input, retrieval from external knowledge sources (often stored in vector databases), contextualization, and generation. The post highlights RAG's advantages for enterprises, such as improved accuracy, domain-specific expertise, adaptability, cost efficiency, and trust. It also touches on key implementation considerations like source quality, relevance, stylistic consistency, balancing retrieval and generation, and evaluation. Finally, it briefly mentions Glean's use of RAG to provide grounded, permission-aware answers.