Explore GraphRAG implementation in this 15-minute conference talk that demonstrates how to enhance retrieval-augmented generation by combining knowledge graphs with traditional RAG approaches. Learn to build knowledge graphs from unstructured text and understand how graph-based context improves retrieval precision and explainability compared to vector search alone. Discover the limitations of vector search and see how graph traversal can provide richer, more transparent context for generative AI applications. Follow along as the presentation shows practical integration of GraphRAG tools into a LangChain agent, with hands-on examples of extracting structured relationships and provenance from unstructured data. Gain insights into when to use vector search versus graph traversal, and understand how knowledge graphs can add structure and reliability to your GenAI projects by providing more grounded and explainable retrieval results.