When Vectors Break Down - Graph-Based RAG for Dense Enterprise Knowledge

Explore a conference talk that addresses the limitations of traditional vector-based retrieval-augmented generation (RAG) systems in enterprise environments. Learn how Writer developed a graph-based RAG architecture that achieved 86.31% accuracy on the RobustQA benchmark while maintaining sub-second response times, significantly outperforming vector approaches when dealing with dense enterprise knowledge bases containing thousands of documents with similar terms. Discover the key techniques behind this performance improvement, including the use of specialized large language models to build semantic relationships, compression techniques for handling concentrated enterprise data patterns, and methods for infusing key data points in the memory layer to reduce hallucination. Understand why graph-based approaches excel with complex enterprise information structures such as product documentation, financial documents, and technical specifications that challenge traditional RAG systems. Gain practical insights into identifying when graph-based approaches make sense for your organization's specific data challenges and learn how to make informed architectural decisions for enterprise RAG systems. The presentation covers Writer's journey in developing this solution and provides actionable guidance for engineers working with enterprise knowledge management systems.