Prompt Engineering with ChatGPT: A Guide for Academic Writers
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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Prompt engineering skill can result in a greater understanding of the capabilities and limitations of LLMs and enhances student self-efficacy by giving them tools to leverage these technologies for their academic pursuits.
“This skill can also result in a greater understanding of the capabilities and limitations of LLMs (Hillier, 2023) and enhances student self-ef fi cacy by giving them the tools to leverage these technologies for their academic pursuits (Giray, 2023).”
From Artificial Intelligence for Academic Purposes (AIAP): Integrating AI literacy into an EAP module (2025) · cites this paper as Giray, 2023
The prompt engineering segment incorporated best practices identified in the literature review, such as the CREATE framework and the importance of clarity, relevance, examples, specifying AI role, iteration, and evaluation in crafting effective prompts.
“The prompt engineering segment incorporated best practices identified in the literature review, such as the CREATE framework (Barrett 2023) and the importance of clarity, relevance, examples, specifying AI role, iteration, and evaluation in crafting effective prompts (Giray 2023;Gruber, n.d.;Jacobs and Fisher 2023;Lo 2023;Mollick 2023;Ryan 2024).”
From Fostering AI Literacy in Undergraduates: A ChatGPT Workshop Case Study (2025) · cites this paper as Giray 2023
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- Authors
- Giray, L.
- Affiliations
- Colegio de Muntinlupa (PH)
- Corresponding author
- Giray, Louie
- ORCID
- Giray, Louie
- Venue
- Annals of Biomedical Engineering
- Published
- 2023-06-07
- Volume
- 51
- Issue
- 12
- Pages
- 2629-2633
- ISSN
- 0090-6964 (ISSN-L)
- 1573-9686
- OpenAlex
- W4379598302
- Cited by
- 776
- References
- 2
- Citations by year
- 2026: 197 · 2025: 341 · 2024: 211 · 2023: 27
- FWCI
- 73.06
- Type
- journal article
- Language
- en
- Open access
- closed
- Licence
- Locations
- Annals of Biomedical Engineering (publishedVersion)
- PubMed (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Topic Modeling
- Social Sciences › Arts and Humanities › History and Philosophy of Science › Academic Writing and Publishing
- Physical Sciences › Computer Science › Artificial Intelligence › Natural Language Processing Techniques
- Keywords
- prompt engineering, ChatGPT, large language models, academic writing, AI-assisted writing, artificial intelligence in research
- MeSH terms
- Artificial Intelligence, Writing
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