  Effective diabetes management is crucial for maintaining health in diabetic
patients. Large Language Models (LLMs) have opened new avenues for diabetes
management, facilitating their efficacy. However, current LLM-based approaches
are limited by their dependence on general sources and lack of integration with
domain-specific knowledge, leading to inaccurate responses. In this paper, we
propose a knowledge-infused LLM-powered conversational health agent (CHA) for
diabetic patients. We customize and leverage the open-source openCHA framework,
enhancing our CHA with external knowledge and analytical capabilities. This
integration involves two key components: 1) incorporating the American Diabetes
Association dietary guidelines and the Nutritionix information and 2) deploying
analytical tools that enable nutritional intake calculation and comparison with
the guidelines. We compare the proposed CHA with GPT4. Our evaluation includes
100 diabetes-related questions on daily meal choices and assessing the
potential risks associated with the suggested diet. Our findings show that the
proposed agent demonstrates superior performance in generating responses to
manage essential nutrients.
