The GraphRAG System That Asks Back: Intelligent Probing with Better Contextual Answers
summary
Graph-powered RAG that builds explicit entity relationships with Neo4j knowledge graph and a retrieval pipeline reduces token waste by avoiding blind top-K searches and lets the system generate targeted hypothetical clarifying questions via a probing agent. Deployed at Cisco and handling day-to-day support cases, the implementation moves vague queries into directed subgraphs, typically resolves intent after one-to-two probes, and reports measurable gains in answer fidelity and dialogue efficiency.
AI-written summary · about
chapters (10)
- 0:00Introduction and welcome
- 1:07Problem with vague user queries
- 4:39RAG basics and production challenges
- 8:29Handling ambiguous WebEx queries
- 11:43Top K vs metadata vs knowledge graph
- 16:16Advantages of knowledge graphs
- 18:26System design with probing agent
- 22:44Live demo with schedule a meeting
- 24:34Production lessons learned
- 27:24Key takeaways and conclusion
related talks
36mAgentic GraphRAG: AI’s Logical Edge
21mAgentic GraphRAG: Autonomous Knowledge Graph Construction and Adaptive Retrieval
30mAgentic RAG with Knowledge Graphs Explained
38mLeveraging Knowledge Graphs for RAG: A Smarter Approach to Contextual AI Applications
12mAgentic GraphRAG: Autonomous Knowledge Graph Construction and Adaptive Retrieval
24mEmpowering Lifelong Learning: Transforming Data into Insights With GraphRAG
25mEnhancing Job Matching With Knowledge Graphs and RAG
01h 26mThinking in Graphs: The Cognitive Foundations of Agentic AI