Knowledge Graphs for RAG

Knowledge Graphs for RAG is a free, ~1-hour intermediate short course from DeepLearning.AI taught by Andreas Kollegger (Neo4j's GenAI developer relations lead), and it is genuinely worth taking if you already know RAG/LLM basics and want to add graph-based retrieval to your toolkit. The hands-on notebooks move from knowledge-graph fundamentals and Cypher querying (on a movie dataset) to building a real RAG knowledge graph from SEC Form 10-K and Form 13 filings, adding vector indexes, and finally chatting with the graph via LangChain's GraphCypherQAChain. On the Coursera mirror it holds a strong 4.8/5 from roughly 100 ratings, with reviewers calling it 'compact but great' and praising the clear, focused instruction. The recurring honest criticism is that it is short and explicitly not for beginners: it does not teach Neo4j or RAG fundamentals from the ground up. There is no certificate (DeepLearning.AI lists it as a non-certificate short course), so the value is purely the skills and code, not a credential.

It is free, taught by an authoritative Neo4j practitioner, and one of the few concise, code-first introductions to combining knowledge graphs with vector search for RAG. For its target audience (developers comfortable with RAG/LLMs and Python) the 1-hour time cost is low and the payoff is a working mental model plus runnable Neo4j + LangChain code, which justifies a clear 'take.'

Best for: Developers, ML/AI engineers, and data practitioners who already understand RAG, LLMs, embeddings, and basic Python, and want a fast, practical on-ramp to graph-based retrieval (Neo4j, Cypher, vector indexes) and GraphRAG patterns. It is especially useful for anyone building question-answering systems over structured or semi-structured documents (e.g., financial/SEC filings).

Skip if: Complete beginners to RAG, LLMs, or databases. The course assumes intermediate background and does not cover Neo4j or RAG fundamentals from scratch, so newcomers will feel it moves too fast. Also not ideal for anyone who needs a shareable certificate, a deep/comprehensive Neo4j course, or production-grade GraphRAG architecture, since it is intentionally a short, introductory overview.