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.