Prompt engineering on leveraging large language models in generating response to InBasket messages.

Objectives: Large Language Models (LLMs) have been proposed as a solution to address high volumes of Patient Medical Advice Requests (PMARs). This study addresses whether LLMs can generate high quality draft responses to PMARs that satisfies both patients and clinicians with prompt engineering.

Materials And Methods: We designed a novel human-involved iterative processes to train and validate prompts to LLM in creating appropriate responses to PMARs.