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Agentic GraphRAG for the real-world

theneuralmaze.substack.com · 1,227 words · saved by 1 readers

In this article, Maikel Gonzalez Baile will introduce us to GraphRAG and Agentic GraphRAG, showcasing a fascinating use case: applying it to security incident analysis. Let’s go! 👇 The Neural Maze is a reader-supported publication. To receive new posts and support my work, consider becoming a free or paid subscriber. Let’s say you’ve set up your RAG pipeline: documents chunked, vector database running, LLM connected. The first query works as expected. Job done, right? But then you ask a real question. One that requires connecting a tiny detail from one document to a concept in another. Suddenly, your brilliant RAG pipeline starts to stutter. It pulls up irrelevant snippets, misses the obvious connection, and gives you an answer that’s confidently … wrong. If you’ve been there, you’ve hit the fundamental wall of classic RAG. It’s great at finding individual pages but has no idea how they link together to tell a bigger story. And that, my friend, is where all the interesting stuff happe

Image by Alina Grubnyak (source: Unsplash) You’ve probably already seen what standard RAG can do. But what if your system could go further than basic semantic search? What if it could understand the connections in your data and reason like a true analyst? In this article, Maikel Gonzalez Baile will introduce us to GraphRAG and Agentic GraphRAG, showcasing a fascinating use case: applying it to security incident analysis. Let’s go! 👇 Let’s say you’ve set up your RAG pipeline: documents chunked, vector database running, LLM connected. The first query works as expected. Job done,…

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