Evaluation of LLMs accuracy and consistency in the registered dietitian exam through prompt engineering and knowledge retrieval
Scientific Reports volume 15, Article number: 1506 (2025 ) Cite this article
Large language models (LLMs) are fundamentally transforming human-facing applications in the health and well-being domains: boosting patient engagement, accelerating clinical decision-making, and facilitating medical education. Although state-of-the-art LLMs have shown superior performance in several conversational applications, evaluations within nutrition and diet applications are still insufficient. In this paper, we propose to employ the Registered Dietitian (RD) exam to conduct a standard and comprehensive evaluation of state-of-the-art LLMs, GPT-4o, Claude 3.5 Sonnet, and Gemini 1.5 Pro, assessing both accuracy and consistency in nutrition queries. Our evaluation includes 1050 RD exam questions encompassing several nutrition topics and proficiency levels. In addition, for the first time, we examine the impact of Zero-Shot (ZS), Chain of Thought (CoT), Chain of Thought with Self Consistency (CoT-SC), and Retrieval Augmented Prompting (RAP) on both accuracy and consistency of the responses. Our findings revealed that while these LLMs obtained acceptable overall performance, their results varied considerably with different prompts and question domains. GPT-4o with CoT-SC prompting outperformed the other approaches, whereas Gemini 1.5 Pro with ZS recorded the highest consistency. For GPT-4o and Claude 3.5, CoT improved the accuracy, and CoT-SC improved both accuracy and consistency. RAP was particularly effective for GPT-4o to answer Expert level questions. Consequently, choosing the appropriate LLM and prompting technique, tailored to the proficiency level and specific domain, can mitigate errors and potential risks in diet and nutrition chatbots.
There is growing interest in leveraging conversational models, commonly known as chatbots, in healthcare, particularly in the areas of diet and nutrition1,2,3. The rise of large language models (LLMs) is significantly transforming human-machine interactions in this context, creating new opportunities for nutrition management applications and lifestyle enhancement that involve natural language understanding and generation4,5,6. These chatbots can serve as assistants to health providers (e.g., dietitian or nurses) or as ubiquitous companions for patients, providing preventive care, personalized meal planning, and chronic disease management7.
