Make LLMs Make Sense with GraphRAG

Learn how to reduce hallucinations and improve LLM outputs through GraphRAG in this 25-minute technical talk by Martin O'Hanlon from Neo4J. Discover how graph databases can enhance retrieval-augmented generation (RAG) to make large language models more accurate and reliable. The presentation offers a fast-paced overview of techniques to level up your generative AI implementations by structuring knowledge in graph form, allowing for more contextual understanding and precise responses. Perfect for developers and AI engineers looking to overcome common limitations in LLM applications and build more trustworthy AI systems.