Prompt design in large language model health coaching chatbots: A scoping review

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Abstract
Artificial intelligence (AI) chatbots are being explored to deliver health coaching across multiple contexts. However, the quality and safety of chatbot-generated advice is influenced by how prompts are constructed, and prompt design remains inconsistently described in the literature. This scoping review examined how prompt design has been applied and evaluated in AI health coaching chatbots and summarized the study designs, prompt strategies, and outcomes reported across studies. We conducted a scoping review following PRISMA-ScR guidance and searched PubMed, Embase/Ovid MEDLINE ALL, PsycINFO, IEEE Xplore, and Scopus for peer-reviewed studies published up to October 2025. Eligible studies examined prompt design in large language model–based health coaching chatbots. Data were extracted using a standardized form and synthesized narratively. Twelve studies were included, spanning mental health, medication use, physical activity, and nutrition contexts. Study designs were primarily simulation-based, with few involving real users. Prompt strategies were grouped into structural modifications, prompting techniques, and role-based approaches. Structural modifications, such as increasing specificity and adding constraints, were most common and were reported to improve response quality and clarity. Prompting techniques were reported to improve task performance but sometimes reduced focus or consistency. Role-based prompting influenced tone, specificity, and guideline alignment. Outcomes focused mainly on response quality, with fewer studies assessing safety or user experience and only one evaluating clinical outcomes. Prompt design is a key, modifiable determinant of chatbot performance. However, current evidence is limited by heterogeneous study designs and minimal real-world evaluation. Greater focus on prompt design is needed to improve the quality and safety of AI-delivered health coaching and its real-world impact on health outcomes.

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