Advanced RAG

What happens when your RAG system retrieves the wrong documents?.

Or when the retrieved context is not enough to answer the question?

A traditional RAG pipeline usually doesn't think twice and its path is so its a single attempt generated answer.

"Retrieve → Generate → Answer"

But real world AI applications aren't always that simple.

Sometimes the system needs to search again in a different way, evaluate what it found, change its approach, verify the answer or even ask for human help before proceeding forward.

That's where Agentic RAG come to the play.

Instead of treating retrieval as a fixed step, Agentic RAG gives the system the ability to reason about the retrieval process and decide what to do next.

Why does Agentic RAG matter?

As RAG applications become more complex with huge documents, simply retrieving the top-k documents isn't always enough to get the accurate results.

Agentic RAG can help systems

1. Decide what information to retrieve
2. Reason over retrieved context
3. Verify whether an answer is supported
4. Retry or refine retrieval when needed
5. Escalate to a human when confidence is low

The goal isn't to add complexity but to make system that is more intelligent, robust and reliable. It's about building RAG systems that can adapt instead of blindly following a fixed pipeline.

What I built

I created a collection of self contained notebooks demonstrating different Agentic RAG patterns with LangGraph. Each notebook focuses on a practical pattern that you can understand, experiment with and adapt to your own AI projects.

Free version:
https://github.com/ChandulaSenevirathna/Agentic_RAG

Advanced version:
https://chandula7.gumroad.com/l/Advanced_RAG_LangGraph_Patterns

If you're building RAG applications, this is a step toward moving beyond:

Retrieve → Generate

and toward systems that can

Retrieve → Reason → Verify → Correct → Answer