The rapid development of Artificial Intelligence (AI)-enabled technologies such as Large Language Models (LLMs) has brought significant benefits across various fields, including behavioral health. However, it is important to note that LLMs are typically trained on general-purpose data, do not explicitly adhere to the guidelines and practices followed by behavioral health professionals, and lack in-the-loop feedback, which can result in inaccurate or unhelpful responses. This project addresses the increasing demand for accountable and scalable behavioral health LLM software to help individuals manage personal challenges and enhance their overall well-being. Drawing on expertise in information systems, sociology, and public health, this research team introduces an Accountable LLM software system specifically designed for behavioral health applications based on Cognitive Behavioral Therapy (CBT). The proposed LLM aims to deliver consistent, high-quality guidance and therapy aligned with established practices. The impacts of this project include improved service delivery and the development of accountable designs for future LLM-based approaches in behavioral health applications. The project aims to develop and evaluate an Accountable LLM based on principles from behavioral science and decision theory, specifically for addressing behavioral health conditions such as depression or anxiety. The proposed Accountable LLM employs an innovative fine-tuning strategy that incorporates