Build Smarter GenAI Apps With Neo4j and GraphRAG
Organizations adopting GenAI often face unreliable outputs, hallucinations, and poor domain understanding due to fragmented structured and unstructured data. These limitations hinder AI adoption, erode trust in AI systems, and prevent teams from realizing tangible business value.
Graph-powered Retrieval Augmented Generation (GraphRAG) addresses these challenges by combining vector search with knowledge graphs and advanced data science techniques to deliver enhanced context, deeper semantic understanding, and real-time insights. In this 30 minute webinar, we’ll show you how to use structured and unstructured data to build knowledge graphs that empower your GenAI to return more accurate insights, better personalization, and production-ready AI applications.
We’ll cover:
- Why traditional RAG falls short when it comes to accuracy and trust
- How to combine vector search and knowledge graphs for rich context
- How to transform structured and unstructured data into connected, AI-ready knowledge
- Practical code examples you can apply immediately
- Best practices from AWS and Neo4j experts for building GenAI and agentic apps
SPEAKER
Andreas Kollegger
Director, Developer Advocacy, Neo4j
Andreas is a technological humanist. Starting at NASA, Andreas designed systems from scratch to support science missions. Then, in Zambia, he built medical informatics systems to apply technology for social good. Now with Neo4j, he is spreading the good news about how to think in graphs and to validate and extend our intuitions about how the world works. Everything is connected.
Jeremy Adams
Senior Developer Advocate, Neo4j
