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

Author(s): Sai Bhargav Rallapalli

Originally published on Towards AI.

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

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.

In this article, the author discusses the limitations of traditional Retrieval-Augmented Generation (RAG), emphasizing how it often leads to irrelevant or inaccurate results due to a lack of self-checking mechanisms. The introduction of Corrective RAG (CRAG) is presented as a solution that improves response accuracy by actively evaluating the relevance of retrieved documents and adjusting queries when necessary. The article outlines the advantages of CRAG, including its ability to ensure high-quality context for generating responses and its implementation using LangChain and LangGraph. The post concludes by advocating for the adoption of CRAG to enhance accountability in AI retrieval processes.

Read the full blog for free on Medium.

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Published via Towards AI