Explainable AI With Knowledge Graphs and RAG
To deliver more accurate, explainable AI, vector search alone doesn’t provide the best results. While that technique improves the probability of a correct response, it still lacks domain-specific context and explainability.
When you combine knowledge graphs and RAG into GraphRAG, you ground your LLM in precise, domain-specific data to:
- Easily trace sources
- Explain retrieval logic
- Avoid hallucinations
Learn the difference GraphRAG makes for your GenAI. Join our webinar to see side-by-side examples of LLM responses that compare using vector search alone and then adding in GraphRAG.
In just 30 minutes, you’ll find out:
- How graphs fit within a RAG architecture
- The fundamental ways that GraphRAG improves retrieval
- How using Graph Data Science, a library of algorithms that helps uncover further hidden relationships in data, further enhances GraphRAG
Don’t settle for incomplete answers. Register to see how GraphRAG can improve your results.
SPEAKER
John Stegeman
Senior Graph Database Product Specialist
John “Steggy” Stegeman is a Senior Graph Database Product Specialist with Neo4j. Prior to joining Neo4j, he held solution architect and consulting roles at Oracle, DXC, Waterline Data, and Hitachi Vantara. John is a self-avowed technology nerd who loves using technology to solve real-world enterprise challenges.
