Intelligence AI with RAG and AWS Workshop

In this hands-on, 2-hour workshop, you'll learn how to build grounded AI applications that combine the power of large language models (LLMs) with the structure and context of knowledge graphs.

We’ll show you how to use Neo4j as a retrieval engine to improve relevance, reduce hallucinations, and support reasoning in your AI workflows. Using Python and the `neo4j-graph-rag` library and AWS services, you'll build Retrieval-Augmented Generation (RAG) pipelines that dynamically fetch the right information at the right time.

You’ll implement vector search, hybrid retrieval, and graph-native techniques to structure your data for better LLM performance. Finally, we’ll introduce an agentic layer where AI agents reason over connected data—not just retrieve it.

By the end, you’ll walk away with practical skills to integrate LLMs and Neo4j into AI systems that are intelligent, explainable, and production-ready.

Space is limited. Make sure to register early.