In response to the crucial need for more transparent and reliable tools based on Large Language Models (LLMs), this work introduces a novel and explainable architectural enhancement to the Retrieval-Augmented Generation (RAG) pipeline. As organizations increasingly rely on LLMs to navigate and interpret vast, unstructured datasets, ensuring the factual accuracy and traceability of generated content has become essential. Our key contribution is designing a system that formally anchors all produced output in a formally verified knowledge base, thereby significantly alleviating challenges related to hallucination and verbosity. This focus on transparency is achieved by providing exact citations to the information used, enabling users to trace the source of generated content. Our work demonstrates significant improvements to chunk retrieval accuracy, achieved using a multi-granularity recursive chunking strategy and re-ranking with maximum information exposure to the LLM. We also provide extensive comparative analysis of various combinations of LLM and embedding models under this enhanced system. Our contributions lay the groundwork for developing more responsible and reliable AI solutions across knowledge-intensive domains, focusing on enhanced information retrieval and transparent response generation.