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REASONING AUGMENTED GENERATION CODE

Traditional Retrieval-Augmented Generation (RAG) systems rely on a two-step process: first, semantic search retrieves documents based on surface-level similarities. Then, a language model generates answers from those documents. While this method works, it often misses deeper contextual insights and can pull in irrelevant information. ReAG – Reasoning Augmented Generation – offers a robust alternative by feeding raw documents directly to the language model, allowing it to assess and integrate the full context. This unified approach leads to more accurate, nuanced, and context-aware responses.

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