This talk features a live demonstration of the Knowledge Graph Builder, a tool designed to transform unstructured data into a knowledge graph. By combining large language models (LLMs) with knowledge graphs, we can enhance retrieval-augmented generation (RAG) beyond simple vector retrievals. Knowledge graphs allow for high-fidelity data representation, providing rich context to improve the accuracy of responses. We’ll explore how LLMs can extract entities and relationships, turning text into graph structures. The session walks you through getting started with the GraphRAG approach, using Neo4j and LangChain, to create powerful AI-driven knowledge workflows.