Building the Knowledge Layer Your Agents Need

Graph RAG and its progression from prototype to production are examined, focusing on how knowledge graphs enhance Retrieval Augmented Generation. Philip Rathle, CTO of Neo4j, explains that Graph RAG leverages the implied structure within unstructured data to improve AI application accuracy. He notes that while vector-based RAG increases accuracy, it lacks deterministic operations and explainability, which knowledge graphs can provide. Success in Graph RAG hinges on addressing high-value business problems and integrating AI with up-to-date, low-latency knowledge. The discussion also covers graph creation tools, highlighting Neo4j's LLM Graph Builder, and explores the potential of graphs to improve agent capabilities by facilitating query generation and providing a unified data view across systems.