Search RAG today and names like Classic RAG, GraphRAG, and Agentic RAG show up all at once. At first glance they can feel like completely different technologies.

But the shared question is simple: "Before the model answers, what information do we find and how do we hand it over?"

Classic RAG: find related docs and feed them in

The most basic RAG searches for document pieces related to the question and puts them into the LLM's input. You see it often in chatbots that must consult outside knowledge like company policies or product manuals.

Understanding the basic RAG retrieval and generation flow first makes every variant much easier.

GraphRAG: when the relationships are the problem

Sometimes it is not similar passages that matter but the relationships between people, organizations, events, and concepts. That is when graph structure helps.

A question like "What supply-chain risks relate to company A?" may need entities and relations scattered across many documents connected together. GraphRAG-style approaches use that relationship structure in retrieval and summarization.

Agentic RAG: search becomes one of the actions

In basic RAG, the retrieval procedure is fairly fixed. Agentic RAG lets the AI decide more on its own: which searches to run from the question, whether more search is needed, whether to use other tools.

In other words, search stops being a single fixed step and becomes one of the agent's jobs.

More complex RAG is not always better

A service with a small document set and simple questions can do fine on basic RAG. Adding a relationship graph or an agent loop also adds build and operations cost plus more ways to fail.

So check the problem before the name:

  • Do simple searches solve the questions?
  • Must relationships across documents be connected?
  • Must the retrieval strategy change with the situation?

The point is picking the structure that fits as problems get complex.

The bottom line: look at your questions before memorizing structures

Instead of memorizing new structures, look at your own questions first. Once you know whether simple search works, relationships must be joined, or searches must be split, the structure to pick decides itself. Running the basic form on small documents first is the fastest path.

References

Go deeper with a course

If you want to grow from Classic RAG into GraphRAG and Agentic RAG on a real design, a hands-on RAG course takes you through each step.