Discover the full power of ChatGPT and turn it into your smartest business partner, creative collaborator, and productivity coach — all with the right...

Background: Artificial intelligence (AI), particularly large language models (LLMs), presents a significant opportunity to transform #MentalHealthcare through scalable, on-demand support. While LLM-powered chatbots may help reduce barriers to care, their integration into clinical settings raises critical concerns regarding safety, reliability, and ethical oversight. A structured framework is needed to capture their benefits while addressing inherent risks. This paper introduces a conceptual model for prompt engineering, outlining core design principles for the responsible development of LLM-based #MentalHealth chatbots. Objective: This paper proposes a comprehensive, layered framework for prompt engineering that integrates evidence-based therapeutic models, adaptive technology, and ethical safeguards. The objective is to propose and outline a practical foundation for developing AI-driven #MentalHealth interventions that are safe, effective, and clinically relevant. Methods: We outline a layered architecture for an LLM-based #MentalHealth chatbot. The design incorporates: (1) an input layer with proactive risk detection; (2) a dialogue engine featuring a user state database for personalization and Retrieval-Augmented Generation (RAG) to ground responses in evidence-based therapies such as Cognitive Behavioral Therapy (CBT), Acceptance and Commitment Therapy (ACT), and Dialectical Behavior Therapy (DBT); and (3) a multi-tiered safety system, including a post-generation ethical filter and a continuous learning loop with therapist oversight. Results: The primary contribution is the framework itself, which systematically embeds clinical principles and ethical safeguards into system design. We also propose a comparative validation strategy to evaluate the framework’s added value against a baseline model. Its components are explicitly m#Apped to the FAITA-MH and READI frameworks, ensuring alignment with current scholarly standards for responsible AI development. Conclusions: The framework offers a practical foundation for the responsible development of LLM-based #MentalHealth support. By outlining a layered architecture and aligning it with established evaluation standards, this work offers guidance for developing AI tools that are technically capable, safe, effective, and ethically sound. Future research should prioritize empirical validation of the framework through the phased, comparative #Approach introduced in this paper.

RAG went from research paper acronym to “every slide deck ever” in about a year. The pattern is...

Enter a prompt to generate multiple text responses. The app will provide diverse and high-quality options while pruning similar or low-quality ones.

The most interesting AI news from the last week

Anthropic's Interactive Prompt Engineering Tutorial - anthropics/prompt-eng-interactive-tutorial

Unlock the power of Veo 3! Discover surprising prompting techniques to create incredible final shots and elevate your video content.