Graph Database - San Francisco

Tired of endlessly tweaking prompts and getting unpredictable results? It’s time to stop treating LLMs like magic boxes and start building real, inspectable systems. In this 2-hour workshop, you’ll learn how to architect AI agents that reason over your data using Retrieval-Augmented Generation (RAG) with Neo4j.

We’ll dive into Python and the `neo4j-graph-rag` library to build RAG pipelines that connect LLMs to both structured and unstructured data, organized as a knowledge graph.

You’ll learn how to structure your content and vector store to make your agent workflows transparent and testable. See exactly what’s being retrieved, trace reasoning paths, and iterate with real feedback—not guesswork.

Then we’ll walk through an agentic example where your LLM does more than generate text—it queries the graph, interprets context, and makes traceable decisions. You’ll see how to debug and improve each step in the pipeline.

By the end, you’ll know how to build intelligent, production-ready AI systems that behave the way you expect—and improve over time.

Register here

We are excited to announce this new hands-on workshop at the Plug and Play space in Sunnyvale on May 6. Please Register on Luma - https://lu.ma/sx4yxpbd

This 2-hour workshop is for developers who want to start building real, grounded AI applications. You’ll learn how to use Neo4j as a retrieval engine for large language models, structuring data to improve relevance, context, and reasoning in AI workflows.
Using Python and the neo4j-graph-rag library, we’ll walk through building Retrieval-Augmented Generation (RAG) pipelines that dynamically fetch the right data at the right time. You’ll implement retrieval techniques like vector search, hybrid search, and graph-native patterns to reduce hallucinations and boost accuracy.
Finally, we’ll introduce an agentic layer, where AI agents don’t just retrieve information but reason over connected data using Neo4j. By the end of the session, you’ll have the skills to integrate LLMs and knowledge graphs into AI systems that are intelligent, explainable, and ready for real-world use.