Philip Rathle traverses from knowledge graphs to LLMs and illustrates how loading the dice with GraphRAG enhances deterministic reasoning, explainability and agency.    

Philip explains why knowledge graphs are a natural fit for capturing data about real-world systems. Starting with Kevin Bacon, he identifies many ‘graphy’ problems confronting us today. Philip then describes how interconnected systems benefit from the dynamism and data network effects afforded by knowledge graphs. 

Next, Philip provides a primer on how Retrieval Augmented Generation (RAG) loads the dice for large language models (LLMs). He also differentiates between vector- and graph-based RAG. Along the way, we discuss the nature and locus of reasoning (or lack thereof) in LLM systems. Philip articulates the benefits of GraphRAG including deterministic reasoning, fine-grained access control and explainability. He also ruminates on graphs as a bridge to human agency as graphs can be reasoned on by both humans and machines. Lastly, Philip shares what is happening now and next in GraphRAG applications and beyond. 

Philip Rathle is the Chief Technology Officer (CTO) at Neo4j. Philip was a key contributor to the development of the GQL standard and recently authored The GraphRAG Manifesto: Adding Knowledge to GenAI (neo4j.com) a go-to resource for all things GraphRAG. 

A transcript of this episode is here.