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...
Important topic recently i was working on RAG project and traditional methods failed when the task was getting complex.
Exactly I faced the same thing thats why i made this content hope you will find some good use of it
Nice content i was on the look for some langgraph content. thnx
Glad this notebooks helped you