RAG or GraphRAG: when each one is worth it

RAG or Graph? I prefer to see this question not as a technology choice, but as an architecture decision.

RAG (retrieval augmented generation) is the assistant that, before answering, looks up the right passages in the company's documents. It works very well when the goal is to retrieve information from documents and knowledge bases.

But what about when you need to go beyond retrieval?

When the subject involves entities, relationships and dependencies, and the questions require connecting information spread across different sources, a Knowledge Graph usually makes much more sense. A Knowledge Graph is a map of relationships: customer, contract, product and supplier as points linked to one another.

That is where the combination gets interesting: RAG plus a Knowledge Graph is what is called GraphRAG.

In practice, I do not see GraphRAG as a replacement for traditional RAG. It is one more strategy, for specific AI scenarios inside companies.

The right question

The right question is not "RAG or Graph?". It is this: how is our business knowledge organized, and what kind of reasoning does the AI need to do?

• Knowledge that lives mainly in documents: RAG.
• Knowledge based on relationships: Knowledge Graph.
• Context, relationships and reasoning over many connections at once: GraphRAG.

In the end, AI architecture is not about picking the most sophisticated technology. It is about picking the approach that best solves the problem, weighing quality, cost, response time, operational complexity and room to evolve.

This is one of the points I consider more and more important in AI Engineering: building systems that do not just answer, but understand the business context they live in.