The Sequence Knowledge #482: An Introduction to Corrective RAG
RAG that uses feedback loops to correct itself.
Today we will Discuss:
An overview of Corrective RAG.
The original paper about Corrective RAG from Google DeepMind and others.
💡 AI Concept of the Day: An Introduction to Corrective RAG
Most people associate RAG with the simple workflow of using a vector database to serialize knowledge use in the interactions with an LLM. However, there are several other patterns that improve on that basic interactions. One of the most interesting ones is known as corrective RAG.
Corrective RAG represents an evolution in retrieval-augmented generation, pushing beyond the boundaries of standard RAG to deliver more accurate and contextually appropriate outputs. While standard RAG enhances LLM responses with external knowledge, Corrective RAG introduces a sophisticated feedback mechanism that scrutinizes and refines the generated content. This iterative approach aims to mitigate hallucinations and inconsistencies that may arise in the initial output, ensuring a higher degree of factual accuracy and coherence.