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.
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...