This paper presents the iterative development of Habit Coach, a GPT-based chatbot designed to support users in habit change through personalized interaction. Employing a user-centered design approach, we developed the chatbot using a Retrieval-Augmented Generation (RAG) system, which enables behavior personalization without retraining the underlying language model (GPT-4). The system leverages document retrieval and specialized prompts to tailor interactions, drawing from Cognitive Behavioral Therapy (CBT) and narrative therapy techniques. A key challenge in the development process was the difficulty of translating declarative knowledge into effective interaction behaviors. In the initial phase, the chatbot was provided with declarative knowledge about CBT via reference textbooks and high-level conversational goals. However, this approach resulted in imprecise and inefficient behavior, as the GPT model struggled to convert static information into dynamic and contextually appropriate interactions. This highlighted the limitations of relying solely on declarative knowledge to guide chatbot behavior, particularly in nuanced, therapeutic conversations. Over four iterations, we addressed this issue by gradually transitioning towards procedural knowledge, refining the chatbot's interaction strategies, and improving its overall effectiveness. In the final evaluation, 5 participants engaged with the chatbot over five consecutive days, receiving individualized CBT interventions. The Self-Report Habit Index (SRHI) was used to measure habit strength before and after the intervention, revealing a reduction in habit strength post-intervention. These results underscore the importance of procedural knowledge in driving effective, personalized behavior change support in RAG-based systems.

翻译：本文介绍了习惯教练的迭代开发过程，这是一个基于GPT的聊天机器人，旨在通过个性化交互支持用户改变习惯。采用以用户为中心的设计方法，我们利用检索增强生成系统开发了该聊天机器人，该系统能够在无需重新训练底层语言模型的情况下实现行为个性化。该系统通过文档检索和专门设计的提示来定制交互，借鉴了认知行为疗法和叙事疗法的技术。开发过程中的一个关键挑战在于将陈述性知识转化为有效的交互行为。在初始阶段，聊天机器人通过参考教科书和高级对话目标获得了关于认知行为疗法的陈述性知识。然而，这种方法导致了不精确和低效的行为，因为GPT模型难以将静态信息转化为动态且情境适宜的交互。这凸显了仅依赖陈述性知识指导聊天机器人行为的局限性，尤其是在微妙复杂的治疗性对话中。经过四次迭代，我们通过逐步转向程序性知识、优化聊天机器人的交互策略并提升其整体有效性，解决了这一问题。在最终评估中，5名参与者连续五天与聊天机器人互动，接受了个性化的认知行为疗法干预。使用自我报告习惯指数在干预前后测量习惯强度，结果显示干预后习惯强度有所降低。这些结果强调了程序性知识在推动基于RAG的系统提供有效、个性化行为改变支持中的重要性。