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Prompt Engineering with ChatGPT: A Guide for Academic Writers

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Abstract

Prompt engineering is a relatively new discipline that refers to the practice of developing and optimizing prompts to effectively utilize large language models, particularly in natural language processing tasks. However, not many writers and researchers are familiar about this discipline. Hence, in this paper, I aim to highlight the significance of prompt engineering for academic writers and researchers, particularly the fledgling, in the rapidly evolving world of artificial intelligence. I also discuss the concepts of prompt engineering, large language models, and the techniques and pitfalls of writing prompts. Here, I contend that by acquiring prompt engineering skills, academic writers can navigate the changing landscape and leverage large language models to enhance their writing process. As artificial intelligence continues to advance and penetrate the arena of academic writing, prompt engineering equips writers and researchers with the essential skills to effectively harness the power of language models. This enables them to confidently explore new opportunities, enhance their writing endeavors, and remain at the forefront of utilizing cutting-edge technologies in their academic pursuits.

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References

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Acknowledgements

The author acknowledges the help of ChatGPT in terms of refining, editing, and augmenting the manuscript.

Funding

This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.

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Correspondence to Louie Giray.

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No benefits in any form have been or will be received from a commercial party related directly or indirectly to the subject of this manuscript. The author declares no conflict of interest.

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This study does not include any individual-level data and thus does not require any ethical approval.

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Associate Editor Stefan M. Duma oversaw the review of this article.

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Giray, L. Prompt Engineering with ChatGPT: A Guide for Academic Writers. Ann Biomed Eng 51, 2629–2633 (2023). https://doi.org/10.1007/s10439-023-03272-4

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  • DOI: https://doi.org/10.1007/s10439-023-03272-4

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