NODES 2025 - The GraphRAG System That Asks Back: Intelligent Probing with Better Contextual Answers

In customer support and technical assistance, resolving user issues is often hindered by incomplete or ambiguous queries. Traditional RAG systems, relying heavily on semantic similarity, struggle to fully grasp the user's intent from vague input, leading to suboptimal answers. This session introduces AID (Assisted Intelligent Dialogue), a sophisticated knowledge system developed at Cisco specifically to overcome these limitations and tackle real-world customer challenges. We will explain how AID enhances standard RAG by leveraging Neo4j graphs to build a deeper multimodal understanding of content and its relationships. A key innovation is AID's intelligent probing mechanism. By using techniques, including generating hypothetical questions, AID first refines the search space within the knowledge graph to identify the most relevant contexts. From these precise subgraphs, AID formulates clarifying questions, effectively ""asking back"" the user to gather crucial missing context and refine their intent through an assisted intelligent dialogue. You will learn how this graph-powered approach directly improves Cisco's ability to solve day-to-day customer issues by transforming unclear inquiries into well-defined problems, resulting in significantly more accurate and actionable contextual answers. The session will cover AID's architecture, its multimodal retrieval, dynamic metadata extraction, and, critically, how Neo4j enables this intelligent probing and dialogue to deliver a superior user experience and enhance support outcomes. Join us to discover how to build AI systems that intelligently interact to solve complex problems, ensuring your users get the precise information they need. Speakers: Rajarshee Dhar & Vivek Singh 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