Engineering Closed-Loop Graph-RAG Systems, Part 1: From Retrieval to Reasoning

This article is part 1 of a 4-part series on 'Engineering Closed-Loop Graph-RAG Systems.'


Most teams don't have a knowledge graph at first. They just have a bunch of documents, a vector DB, a chunking method, and a prompt. That's fine. With a simple retrieval-augmented generation system, you can get answers to many practical questions, such as "Where is the deployment guide?" "How does this policy read?" "Which API parameter controls retries?" For those types of uses, flat RAG is almost always sufficient. The system will take your question, find the best matching chunk(s), provide some context to the language model, and let the language model generate an answer.

It doesn't go wrong until you want the system to reason over relationships.