After introducing the core ideas of GraphRAG in Part 1, this follow-up session moves into practice. We’ll take a closer look at Microsoft GraphRAG, Microsoft’s open-source framework for graph-based retrieval workflows, and explore how connected knowledge can improve context, traceability, and explainability in GenAI solutions.
Online Event: GraphRAG in Practice: When Vector Search Is Not Enough.
I’m happy to share that I’ll be speaking again at the Azure User Group Munich, as part of our recurring meetup series held every second Tuesday of the month. 🎤 In my next session, I’ll talk about GraphRAG that I find especially interesting right now. Many current AI solutions are built on vector search and […]
GraphRAG Explained: The Missing Layer in Modern RAG Systems
Retrieving relevant information using vector similarity is the foundation of most RAG systems. While effective for many use cases, it often struggles when answers require connecting multiple pieces of information or understanding relationships across data.
GraphRAG addresses this limitation by organizing data into a knowledge graph. Instead of retrieving isolated text, it captures how entities are connected, enabling more structured reasoning.
In this article, I introduce GraphRAG through a hands-on demo project.