Don’t Prompt Harder. Architect Smarter!

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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.