Jan 16
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As GraphRAG becomes a popular method for grounding LLMs in enterprise knowledge, graph developers and data architects are increasingly asked to make their knowledge graphs “AI-ready.” Yet most graphs, especially those built through automatic or ad hoc methods, contain hidden quality issues that undermine LLM performance. These issues often go unnoticed until they manifest as vague answers, poor retrieval, or untraceable hallucinations. In this hands-on workshop, Panos Alexopoulos will guide graph practitioners, data modelers, and AI engineers through the process of assessing and improving knowledge graph quality for GraphRAG pipelines. Using real-world examples, you will learn how semantic structure, lexical clarity, and modeling consistency influence grounding outcomes, and how to spot the silent failures that disrupt them. By the end, you will leave with a reusable knowledge graph evaluation checklist, a deeper understanding of semantic design trade-offs, and strategies for proactively improving the reliability of your LLM-augmented systems. Speaker: Panos Alexopoulos Resources: Get Started with Aura - https://bit.ly/3LOLrjh Deployment Center - https://bit.ly/4jOelM3 Ground AI Systems and Agents with Neo4j - https://bit.ly/4oVsnyb #nodes2025 #neo4j #graphdatabase #graphrag #knowledgegraph