The scale of biomedical knowledge, spanning scientific literature and curated knowledge bases, poses a significant challenge for investigators in processing, evaluating, and interpreting findings effectively. Large Language Models (LLMs) have emerged as powerful tools for navigating this complex knowledge landscape but may produce hallucinatory responses. Retrieval-Augmented Generation (RAG) is essential for identifying relevant information to enhance accuracy and reliability. This protocol introduces RUGGED (Retrieval Under Graph-Guided Explainable disease Distinction), a comprehensive workflow designed to support knowledge integration, to mitigate bias, and to explore and validate new research directions. Biomedical information from publications and knowledge bases are synthesized and analyzed through text-mining association analysis and explainable graph prediction models to uncover potential drug-disease relationships. These findings, along with the source text corpus and knowledge bases, are incorporated into a framework that employs RAG-enhanced LLMs to enables users to explore hypotheses and investigate underlying mechanisms. A clinical use case demonstrates RUGGED's capability in evaluating and recommending therapeutics for Arrhythmogenic Cardiomyopathy (ACM) and Dilated Cardiomyopathy (DCM), analyzing prescribed drugs for molecular interactions and potential new applications. The platform reduces LLM hallucinations, highlights actionable insights, and streamlines the investigation of novel therapeutics.