Corrective RAG: How to Build Self-Correcting Retrieval-Augmented Generation

Author(s): Sai Bhargav Rallapalli

Originally published on Towards AI.

Retrieval-Augmented Generation (RAG) has completely transformed how we build Large Language Model (LLM) applications. It gives LLMs the superpower to fetch external knowledge and generate context-rich answers.

But here’s the problem →Traditional RAG is like a GPS that always trusts the first route it shows → even if there’s a traffic jam.

It doesn’t check if the retrieved documents are relevant or accurate. If the system pulls poor-quality documents, the response will be poor too. It’s like building a house with bad bricks.

That’s where Corrective RAG (CRAG) steps in.

Non members can read it here.

CRAG is like Google Maps with live traffic.It actively checks the route (retrieved documents), reroutes if needed, and makes sure you reach the right destination (a correct, helpful answer).

In this blog, let’s break down:

Why Corrective RAG mattersHow it actually worksStep-by-step guide to build CRAG using LangChain & LangGraph

Corrective RAG (CRAG) is a smarter version of traditional RAG that:

Grades the retrieved documents to check if they are useful.Automatically rewrites queries or performs web searches if retrieval fails.Ensures the final answer is backed by accurate, relevant context.

Traditional RAG is like asking a random stranger for directions and blindly following them.Corrective RAG is like cross-checking directions on Google Maps, and asking a… Read the full blog for free on Medium.

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Published via Towards AI