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Their fast pace of adoption and probable effect has also given rise to controversy within certain fields. Models, such as GPT-3, are a particular concern for professionals engaged in writing, particularly as their engagement with these technologies is limited due to lack of ability to control their output. Most efforts to maximize and control output rely on a process known as prompt engineering, the construction and modification of the inputted prompt with expectation for certain outputted or desired text. Consequently, prompt engineering has emerged as an important consideration for research and practice. Previous conceptions of prompt engineering have largely focused on technical and logistic modifications to the back-end processing, remaining inaccessible and, still, limited for most users. In this paper, we look to the technical communication field and its methods of text generation\u2014the rhetorical situation\u2014to conceptualize prompt engineering in a more comprehensible way for its users by considering the context and rhetoric. We introduce a framework, consisting of a formula, to prompt engineering, which demands all components of the rhetorical situation be present in the inputted prompt. We present discussions on the future of AI writing models and their use in both professional and educational settings. Ultimately, this discussion and its findings aim to provide a means of integrating agency and writer-centric methods to AI writing tools to advance a more human-in-the-loop approach. As the use of generative AI and especially NLP-based technologies become common across societal functions, the use of prompt engineering will play a crucial role not just in adoption of the technology, but also its productive and responsible use.<\/jats:p>","DOI":"10.1007\/s00146-024-01905-3","type":"journal-article","created":{"date-parts":[[2024,4,1]],"date-time":"2024-04-01T09:12:10Z","timestamp":1711962730000},"page":"711-732","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":23,"title":["Using rhetorical strategies to design prompts: a human-in-the-loop approach to make AI useful"],"prefix":"10.1007","volume":"40","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-0661-3898","authenticated-orcid":false,"given":"Nupoor","family":"Ranade","sequence":"first","affiliation":[]},{"given":"Marly","family":"Saravia","sequence":"additional","affiliation":[]},{"given":"Aditya","family":"Johri","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2024,4,1]]},"reference":[{"key":"1905_CR1","unstructured":"AIContentfy team (2023, October 7) The future of writing: are AI tools replacing human writers? https:\/\/aicontentfy.com\/en\/blog\/future-of-writing-are-ai-tools-replacing-human-writers"},{"key":"1905_CR2","doi-asserted-by":"crossref","unstructured":"Aky\u00fcrek E, Bolukbasi T, Liu F, Xiong B, Tenney I, Andreas J, Guu K (2022) Tracing knowledge in language models back to the training data. 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