Utilizing Engineering Principles in Content Creation

Notice

This is an unedited manuscript accepted for publication and provided as an Article in Press for early access at the author’s request. The article will undergo copyediting, typesetting, and galley proof review before final publication. Please be aware that errors may be identified during production that could affect the content. All legal disclaimers of the journal apply.

Year : 2025 | Volume : 15 | 02 | Page :
    By

    Mr. Virat Rehani,

  • Ms. Saravjit Kaur,

  • Mr. Amit Virdi,

  • Ms. Aditi,

  • Ms. Anju,

  1. Assistant Professor, Computer Applications, Punjab, India
  2. Assistant Professor, Computer Applications, Punjab, India
  3. Assistant Professor, Computer Applications, Punjab, India
  4. Assistant Professor, Computer Applications, Punjab, India
  5. Assistant Professor, Computer Applications, Punjab, India

Abstract

In creative writing and content creation tasks, individuals often utilize prompt chaining to craft narratives, develop characters, and explore diverse storytelling paths. The integration of AI has revolutionized content creation, reshaping the processes of generation, optimization, and refinement. Prompt engineering emerges as a crucial practice, involving the careful crafting and fine-tuning of prompts or instructions to elicit precise and valuable responses from generative AI models. This strategic approach translates human intentions and business goals into actionable outcomes, ensuring alignment with desired objectives. Through systematic refinement, prompt engineering enables more effective collaboration between humans and machines, enhancing creativity while maintaining consistency and coherence. The following application exemplifies the significance of prompt engineering in the realm of content creation, demonstrating its potential to streamline workflows and elevate the overall quality of generated content. By tailoring prompts to specific needs, content creators can not only optimize their creative processes but also achieve higher levels of engagement and relevance in their work. Moreover, as AI continues to evolve, the role of prompt engineering will likely expand, offering new opportunities for fine-tuning and improving outputs in increasingly complex and dynamic content creation environments. This evolving practice promises to further bridge the gap between human creativity and AI-generated innovation.tank

Keywords: Prompt Engineering, Content Creation, Prompt Chaining, ChatGPT, Copilot, Midjourney, Speed, model, rue base, AI.

How to cite this article:
Mr. Virat Rehani, Ms. Saravjit Kaur, Mr. Amit Virdi, Ms. Aditi, Ms. Anju. Utilizing Engineering Principles in Content Creation. OmniScience: A Multi-disciplinary Journal. 2025; 15(02):-.
How to cite this URL:
Mr. Virat Rehani, Ms. Saravjit Kaur, Mr. Amit Virdi, Ms. Aditi, Ms. Anju. Utilizing Engineering Principles in Content Creation. OmniScience: A Multi-disciplinary Journal. 2025; 15(02):-. Available from: https://journals.stmjournals.com/osmj/article=2025/view=235225


References

[1]. Popli N. How to Get a Six-Figure Job as an AI Prompt Engineer [Internet]. Time. 2023. Available from: https://time.com/6272103/ai-prompt-engineer-job/

[2]. Brown TB. Language models are few-shot learners. arXiv preprint arXiv:2005.14165. 2020.

[3]. Petroni F, Rocktäschel T, Lewis P, Bakhtin A, Wu Y, Miller AH, Riedel S. Language models as knowledge bases?. arXiv preprint arXiv:1909.01066. 2019 Sep 3.

[4]. Raffel C, Shazeer N, Roberts A, Lee K, Narang S, Matena M, Zhou Y, Li W, Liu PJ. Exploring the limits of transfer learning with a unified text-to-text transformer. Journal of machine learning research. 2020;21(140):1-67.

[5]. Bender EM, Friedman B. Data statements for natural language processing: Toward mitigating system bias and enabling better science. Transactions of the Association for Computational Linguistics. 2018 Dec 1;6:587-604.

[6]. Holtzman A, Buys J, Du L, Forbes M, Choi Y. The curious case of neural text degeneration. arXiv preprint arXiv:1904.09751. 2019 Apr 22.

[7]. Fan A, Lewis M, Dauphin Y. Hierarchical neural story generation. arXiv preprint arXiv:1805.04833. 2018 May 13.

[8]. Adiwardana D, Luong MT, So DR, Hall J, Fiedel N, Thoppilan R, Yang Z, Kulshreshtha A, Nemade G, Lu Y, Le QV. Towards a human-like open-domain chatbot. arXiv preprint arXiv:2001.09977. 2020 Jan 27.

[9]. Dodge J, Gururangan S, Card D, Schwartz R, Smith NA. Show your work: Improved reporting of experimental results. arXiv preprint arXiv:1909.03004. 2019 Sep 6.

[10]. Gebru T, Morgenstern J, Vecchione B, Vaughan JW, Wallach H, III HD, et al. Datasheets for datasets. Communications of the ACM. 2021 Dec;64(12):86–92.


Ahead of Print Subscription Review Article
Volume 15
02
Received 15/10/2024
Accepted 30/12/2025
Published 30/12/2025
Publication Time 441 Days


Login


My IP

PlumX Metrics