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"
What I built
Retrieve → Reason → Verify → Correct → Answer
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.
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"
What I built
Retrieve → Reason → Verify → Correct → Answer
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_Patte...
Hey thankyou for the info, I was wondering if you can add a notebook about supervisor agent as well.
Hi, yes i am currently working on that pattern as well it will be added as well
Useful content for my current project thanx for posting
Glad this helped you
nice content very informative keep it up
Thankyou