Generative AI & GraphRAG

Graphs, RAG, and grounded AI

Large language models are fluent but forgetful. They hallucinate facts, lose track of how things connect, and cannot cite where an answer came from. A knowledge graph gives them a memory: a structured, queryable store of facts and the relationships between them. Retrieval-augmented generation (RAG) built on a graph retrieves that structured context at query time, so the model answers from grounded, traceable data instead of guessing.

This is where Neo4j and generative AI meet. These courses teach you to build GraphRAG pipelines — combining vector search over unstructured text with graph traversal over structured relationships — and to expose your graph to agents and LLM tools so they can reason over connected data.

They are built for developers and data teams shipping AI features who want answers that are accurate, explainable, and current. You will get the most from them if you are comfortable in Python and have written a little Cypher, but the fundamentals course assumes no prior graph experience. If you have built a basic RAG prototype and watched it hallucinate, this is the path that fixes it.