Thinking in Graphs, Reasoning with Gemini!

In today’s AI landscape, retrieval is no longer enough, understanding relationships is the real differentiator. This session explores how Google Gemini transforms traditional Retrieval-Augmented Generation (RAG) by becoming the reasoning engine behind graph-powered knowledge systems.

We will dive into how GraphRAG, built on Neo4j, enables structured, relationship-aware retrieval allowing AI systems to move beyond isolated chunks of data and instead navigate rich, connected knowledge. At the center of this architecture, Gemini synthesizes context, interprets complex graph relationships, and generates responses that are not only accurate but deeply contextual. Through real-world examples and architecture walkthroughs, you will see how combining graph databases with advanced LLM reasoning unlocks: - More precise and explainable answers - Better handling of multi-hop and relational queries - Scalable, context-rich AI applications

Whether you are building intelligent assistants, knowledge platforms, or next-gen search systems, this session will show how to think in graphs and reason with Gemini to create smarter, more connected AI experiences.