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.

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

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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