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...
Agentic RAG is very useful when dealing with complex tasks and information. Nice work ...
Yes agentic rag is helpful when an answer cannot be found in first try or when you need an fallback methods.