Michael Hunger of Neo4j, joins Simon Maple to unpack how graph databases inject structure, intent, and traceability into modern AI systems. On the docket: why relationships in data encode intent the black-box problem in vector based RAG why devs should build their own MCP server AI Native Dev, powered by Tessl and our global dev community, is your go-to podcast for solutions in software development in the age of AI. Tune in as we engage with engineers, founders, and open-source innovators to talk all things AI, security, and development. Connect with us here: Michael Hunger- https://www.linkedin.com/in/jexpde/ Simon Maple- https://www.linkedin.com/in/simonmaple/ Tessl- https://www.linkedin.com/company/tesslio/ AI Native Dev- https://www.linkedin.com/showcase/ai-native-dev/ (00:00) Trailer (01:03) Introduction & Neo4j Origins (03:02) Persisting Relationships for High-Performance Queries (04:00) Modeling Business Intent & Key Use Cases (05:00) Fraud Detection at Scale with Graph Algorithms (06:11) Graph-Enhanced RAG vs. Vector-Only Retrieval (09:02) Explainability & Drill-Down Evaluation in RAG (13:05) Fusing Structured & Unstructured Data for Context (15:00) MCP for Developer Productivity: Schema-to-Code & API Wrapping (21:16) Security & Sandboxing Best Practices for MCP (29:08) MCP Server Recommendations & Outro Join the AI Native Dev Community on Discord: https://tessl.co/4ghikjh Ask us questions:
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