Summary:
In the rapidly advancing landscape of syndromic surveillance, the incorporation of Large Language Model (LLM) tools, such as ChatGPT, has emerged as a pivotal development, offering a paradigm shift in the domain of prompt engineering. This training session seeks to furnish epidemiologists, public health researchers, and related professionals with a nuanced, holistic understanding of the underpinnings of LLM functionalities and their transformative implications for public health surveillance. One of the crowning elements of our training is the unveiling Public Health Prompt Generator tool designed to seamlessly align with the evolving needs and challenges faced by today’s epidemiological researchers. Through interactive segments, participants will engage in hands-on exercises to hone attendee capability to generate LLM prompts and apply their to create ESSENCE syntax tailored for syndromic surveillance queries. Beyond the primary mechanics of prompt generation, the session will delve deeper into the art and science of prompt design, illuminating strategies that enhance efficiency in literature review processes and aid query development. We aim to ensure that by the conclusion of our session, attendees will learn tested methods for LLM-driven prompt engineering to harness ChatGPT’s expansive and diverse application, ultimately resulting in elevated research quality and outcomes.
Key Objectives: By the end of this training, participants will:
Authors: Jeremy Funk, Andzelika Rzucidlo, Andrew Manigault