What sets great retrieval augmented generation apart — and why vector search isn’t enough for AI

This post details Glean's approach to Retrieval Augmented Generation (RAG) for enterprise search, emphasizing the limitations of simple vector or lexical search alone. It highlights Glean's hybrid search strategy, powered by a knowledge graph framework with proprietary anchors and signals, a scalable crawler, and optimizations for the LLM search interface. The post contrasts Glean's contextually rich, permission-aware responses with standard RAG solutions, showcasing how its hybrid approach, combining semantic and keyword matching with deep contextual understanding, delivers superior relevance and precision for enterprise use cases. It also touches on the importance of proprietary data for fine-tuning models and improving embedding scores.