This session presents the paper 'Talking to Patient Records,' an advanced Retrieval-Augmented Generation (RAG) chatbot designed to enhance healthcare information retrieval by integrating natural language processing with domain-specific knowledge bases to allow clinicians, researchers, and administrators to query patient records conversationally. By combining large language models with RAG techniques, the chatbot delivers accurate, context-aware, and secure responses, reducing the time required to locate critical patient information. The study outlines the system’s architecture, implementation, and potential applications in clinical decision support, patient engagement, and healthcare data management.