Advanced RAG: Comparing GraphRAG, Corrective RAG, and Self-RAG

Author(s): Abduldattijo

Originally published on Towards AI.

You’re Doing RAG Wrong. Let’s Talk About a Real Production System.

Stop. Just… stop.

The article critiques the common missteps in implementing Retrieval-Augmented Generation (RAG), specifically how basic approaches can lead to unreliable outputs in real-world applications. It introduces advanced RAG techniques, such as GraphRAG, Corrective RAG, and Self-RAG, which aim to enhance the accuracy and reliability of AI systems by addressing the complexities of data relationships, incorporating evaluation steps to filter out incorrect information, and ensuring that the AI can actively engage with its processes to prevent erroneous answers from being generated.

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Published via Towards AI