This paper is in the following e-collection/theme issue:

Applications of AI (272) Generative Language Models Including ChatGPT (670) Machine Learning (2034) Chatbots and Conversational Agents (716) Digital Mental Health Interventions, e-Mental Health and Cyberpsychology (2068) Tools, Programs and Algorithms (368) Quality/Credibility of eHealth Information and Trust Issues (646) Psychotic Disorders (193) Telepsychiatry (207) Responsible Health AI (47)

Published on in Vol 4 (2025)

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/69820, first published .
Prompt Engineering an Informational Chatbot for Education on Mental Health Using a Multiagent Approach for Enhanced Compliance With Prompt Instructions: Algorithm Development and Validation

Prompt Engineering an Informational Chatbot for Education on Mental Health Using a Multiagent Approach for Enhanced Compliance With Prompt Instructions: Algorithm Development and Validation

Prompt Engineering an Informational Chatbot for Education on Mental Health Using a Multiagent Approach for Enhanced Compliance With Prompt Instructions: Algorithm Development and Validation

Authors of this article:

Per Niklas Waaler1 Author Orcid Image ;   Musarrat Hussain1 Author Orcid Image ;   Igor Molchanov1 Author Orcid Image ;   Lars Ailo Bongo1 Author Orcid Image ;   Brita Elvevåg2 Author Orcid Image
Article Authors Cited by (2) Tweetations (2) Metrics

Journals

  1. Boit S, Patil R. A Prompt Engineering Framework for Large Language Model–Based Mental Health Chatbots: Design Principles and Insights for AI-Supported Care (Preprint). JMIR Mental Health 2025 View
  2. Zhang R, Meng H, Neubronner M, Lee Y. Computational and ethical considerations for using large language models in psychotherapy. Nature Computational Science 2025;5(10):854 View