Graph RAG: How To Squeeze More Value From AI

Graph retrieval-augmented generation (RAG) is gaining popularity as a way to enhance traditional vector search by incorporating structured, graph-based data. This approach allows for better handling of complex queries that require aggregating information across diverse knowledge bases. Glean, an enterprise platform using graph RAG, has found success in streamlining internal processes and saving time and money for its customers. While getting started with graph RAG is relatively easy, challenges arise when moving from research and development to production. Key factors for a successful implementation include accurately capturing and leveraging non-semantic relationships among data, maintaining simplicity while maximizing efficiency, and ensuring the quality of the knowledge graph. As AI technology continues to evolve, graph RAG systems are expected to play an increasingly important role in enhancing retrieval capabilities and revolutionizing knowledge management across various industries.