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A Prompt Engineering Framework for Large Language Model-Based Mental Health Chatbots: Conceptual Framework.

Sorio Boit¹, Rajvardhan Patil¹

¹Department of Computer Science, College of Computing, Grand Valley State University, 1 Campus Dr, Allendale, MI, 49401, United States, 1 616-331-4375.

JMIR Mental Health

|November 7, 2025

Summary

This study introduces the MIND-SAFE framework for developing safe and effective AI mental health chatbots. It outlines prompt engineering principles and a layered architecture to ensure ethical AI in mental healthcare.

Keywords:

AI in mental health care MIND-SAFE framework artificial intelligence conversational AI digital mental health ethical AI large language model mental health chatbot prompt engineering

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Published on: June 13, 2025

Area of Science:

Mental Health Technology
Artificial Intelligence in Healthcare
Clinical Psychology

Background:

Large language models (LLMs) offer scalable mental health support but raise safety and ethical concerns.
A structured framework is essential for responsible AI development in mental healthcare.
Prompt engineering principles are key to mitigating risks associated with LLM-based chatbots.

Purpose of the Study:

Propose the Mental Well-Being Through Dialogue - Safeguarded and Adaptive Framework for Ethics (MIND-SAFE).
Provide a practical foundation for developing safe, effective, and clinically relevant AI mental health interventions.
Integrate evidence-based therapeutic models, adaptive technology, and ethical safeguards into AI chatbot design.

Main Methods:

Outline a layered architecture for an LLM-based mental health chatbot.
Implement proactive risk detection in the input layer.
Utilize a dialogue engine with user state personalization and retrieval-augmented generation for evidence-based therapy grounding (CBT, ACT, DBT).
Incorporate a multitiered safety system with ethical filters and therapist oversight.

Main Results:

The MIND-SAFE framework systematically embeds clinical principles and ethical safeguards into AI system design.
A comparative validation strategy is proposed to assess the framework's added value.
Framework components align with established standards for AI in mental health (e.g., FAT-MH, REAM-DEI).

Conclusions:

The MIND-SAFE framework provides a foundation for responsible LLM-based mental health support development.
Guidance is offered for creating technically capable, safe, effective, and ethical AI mental health tools.
Empirical validation via a phased, comparative approach is recommended for future research.