Connecting the dots for accurate AI

At HumanX, Neo4j CTO Philip Rathle discussed with Ryan how knowledge context enhances AI agents, arguing that a model-only approach is unsuitable for enterprises due to issues like stale training data. He explained that Graph RAG improves accuracy and reduces "context rot" by combining vectors with a knowledge graph, making agents more targeted and connected.

At HumanX, Ryan is joined by Philip Rathle, CTO at Neo4j to discuss what knowledge context means for AI agents, how limitations like stale training data make the model-only approach to agents a bad fit for enterprise environments, and how Graph RAG raises the bar for accuracy and reduces context rot by combining vectors with a knowledge graph so agents are more targeted and connected. Neo4j is a native graph database management system designed to handle complex, highly-connected data by focusing on relationships rather than tables. You can try it out for free on Aura and learn more at their Graph Academy.