Introduction to Corrective RAG
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.
The Problem with Traditional RAG
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.
What is Corrective RAG (CRAG)?
That’s where Corrective RAG (CRAG) steps in. 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).
Key Features of CRAG
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.
How CRAG Works
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 local for confirmation. CRAG gives you a more accurate and reliable answer.
Building CRAG using LangChain & LangGraph
In this blog, let’s break down:
- Why Corrective RAG matters
- How it actually works
- Step-by-step guide to build CRAG using LangChain & LangGraph
Conclusion
Corrective RAG (CRAG) is a game-changer for Large Language Model (LLM) applications. It ensures that the retrieved documents are relevant, accurate, and useful, providing a more reliable and accurate answer. With CRAG, you can build more efficient and effective LLM applications.
Frequently Asked Questions (FAQs)
- Q: What is the main difference between Traditional RAG and Corrective RAG?
- A: Traditional RAG doesn’t check the quality of retrieved documents, while Corrective RAG grades and verifies the documents to ensure accuracy and relevance.
- Q: How does CRAG improve the accuracy of LLM applications?
- A: CRAG improves accuracy by re-routing and re-checking the retrieved documents, ensuring that the final answer is backed by accurate and relevant context.
- Q: Can I build CRAG using LangChain & LangGraph?
- A: Yes, you can build CRAG using LangChain & LangGraph. A step-by-step guide is available to help you get started.
