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

ABSTRACT:

Background

People with schizophrenia often present with cognitive impairments that may hinder their ability to learn about their condition. Education platforms powered by large language models (LLMs) have the potential to improve the accessibility of mental health information. However, the black-box nature of LLMs raises ethical and safety concerns regarding the controllability of chatbots. In particular, prompt-engineered chatbots may drift from their intended role as the conversation progresses and become more prone to hallucinations.

Objective

This study aimed to develop and evaluate a critical analysis filter (CAF) system that ensures that an LLM-powered prompt-engineered chatbot reliably complies with its predefined instructions and scope while delivering validated ment

SUBMITTER: Waaler PN 

PROVIDER: S-EPMC11982747 | biostudies-literature | 2025 Mar

REPOSITORIES: biostudies-literature