Smarter RAG Responses: How Future AGI Reduces Hallucinations

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Smarter RAG Responses: How Future AGI Reduces Hallucinations

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Future AGI 27 May 2025

Retrieval-Augmented Generation (RAG) is a hybrid architecture that combines a retriever and a generator to improve the factual accuracy and relevance in responses of a language model.

Future AGI enhances Retrieval-Augmented Generation (RAG) by reducing hallucinations—false but plausible outputs—through a robust evaluation framework. It focuses on three key metrics: Groundedness (response alignment with retrieved content), Context Adherence (staying within provided information), and Retrieval Quality (relevance and completeness of retrieved documents). By optimizing pipeline configurations such as character-based chunking, Maximal Marginal Relevance (MMR) retrieval, and map-rerank generation chains, Future AGI ensures more accurate and reliable AI outputs. This systematic approach significantly improves the factual consistency of RAG systems, making them more suitable for high-stakes and mission-critical applications that demand precision and trustworthy responses.