Large language models (LLMs) show promise in clinical decision support but are limited by hallucinations and explainability. This project investigates how different retrieval-augmented generation (RAG) architectures can improve the accuracy, transparency, and clinical reliability of diabetes-related responses, with the ultimate goal of developing a deployable clinical model for diabetic care. We implement and compare standard RAG and graph-based RAG systems that integrate the Medical Information Mart for Intensive Care III (MIMIC-III) database with a locally hosted Ollama LLM. Retrieved clinical records and structured relationships are used to ground model outputs in real patient data. The system is evaluated using Phoenix Arize to trace retrieval pathways, visualize evidence chains, quantify hallucination rates, and monitor response accuracy. By grounding responses in verifiable clinical data and enabling transparent reasoning traces, this work contributes to the development of safer and more explainable artificial intelligence systems for healthcare applications. This project will lay the foundation of an agentic AI-based “Diabetes Coach,” a conversational system aimed at supporting adult patients (aged 18 and older) diagnosed with type 2 diabetes.
