Retrieval-augmented generation (RAG) improves large language models by fetching relevant documents from an external source to support text generation. However, RAG isn’t perfect—it can still produce misleading content if the retrieved documents aren't accurate or relevant.
To overcome these problems, corrective retrieval-augmented generation (CRAG) was proposed. CRAG works by adding a step to check and refine the information retrieved before using it to generate text. This makes the language models more accurate and reduces the chance of generating misleading content.
In this article, I’ll introduce CRAG and guide you through a step-by-step implementation using LangGraph.
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