Mr. Chandrashekhar Medicherla

Title of the Talk :
Beyond Vector Search: Hybrid RAG for Context-Aware AI Systems

Abstract of Talk:
Traditional vector-based RAG systems excel at finding semantically similar content but fundamentally lack understanding of relationships, dependencies, and contextual connections that are critical for solving complex real-world problems. This talk introduces Hybrid RAG, an advanced architecture that combines Graph RAG for modeling interconnected knowledge structures, MCP (Model Context Protocol) servers for accessing live data and system state, and traditional semantic search to deliver truly context-aware AI responses. By integrating structured relationship knowledge with real-time evidence and semantic retrieval, Hybrid RAG eliminates hallucinations and generic answers, instead providing precise, traceable, and actionable insights grounded in verifiable sources. Attendees will learn architectural patterns, implementation strategies, and practical use cases for building reliable AI systems that understand not just content similarity, but the critical context, causality, and temporal relationships that drive accurate decision-making in domains like incident response, root cause analysis, and complex troubleshooting scenarios.