RAG vs. Agentic RAG vs. Graph RAG: Which One Actually Fits Your Use Case?

If you’ve built anything with LLMs in the last couple of years, you’ve built a RAG pipeline. Embed the query, search a vector store, stuff the top chunks into a prompt, let the model talk. It’s the “Hello World” of grounding LLMs in real data – and for a long time, it was enough.

It isn’t anymore.

The moment your use case involves multi-hop reasoning, tool calls, or relationships between entities scattered across thousands of documents, naive RAG starts cracking. That’s given rise to two evolutions worth understanding deeply: Agentic RAG and Graph RAG. They solve different problems, and confusing them will cost you weeks of rebuilding. Let’s walk through all three, step by step.

A breakdown of three retrieval architectures: RAG, Agentic RAG, Graph RAG - why "just add RAG" stopped being good enough advice a while ago

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