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Liczba wyników
2023 | 11 | nr 3 | 25--37
Tytuł artykułu

Artificial Intelligence Prompt Engineering as a New Digital Competence : Analysis of Generative AI Technologies such as ChatGPT

Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
Objective: The article aims to offer a thorough examination and comprehension of the challenges and prospects connected with artificial intelligence (AI) prompt engineering. Our research aimed to create a theoretical framework that would highlight optimal approaches in the field of AI prompt engineering.
Research Design & Methods: This research utilized a narrative and critical literature review and established a conceptual framework derived from existing literature taking into account both academic and practitioner sources. This article should be regarded as a conceptual work that emphasizes the best practices in the domain of AI prompt engineering.
Findings: Based on the conducted deep and extensive query of academic and practitioner literature on the subject, as well as professional press and Internet portals, we identified various insights for effective AI prompt engineering. We provide specific prompting strategies.
Implications & Recommendations: The study revealed the profound implications of AI prompt engineering across various domains such as entrepreneurship, art, science, and healthcare. We demonstrated how the effective crafting of prompts can significantly enhance the performance of large language models (LLMs), generating more accurate and contextually relevant results. Our findings offer valuable insights for AI practitioners, researchers, educators, and organizations integrating AI into their operations, emphasizing the need to invest time and resources in prompt engineering. Moreover, we contributed the AI PROMPT framework to the field, providing clear and actionable guidelines for text-to-text prompt engineering.
Contribution & Value Added: The value of this study lies in its comprehensive exploration of AI prompt engineering as a digital competence. By building upon existing research and prior literature, this study aimed to provide a deeper understanding of the intricacies involved in AI prompt engineering and its role as a digital competence. (original abstract)
Rocznik
Tom
11
Numer
Strony
25--37
Opis fizyczny
Twórcy
  • Kozminski University, Poland
  • Kozminski University, Poland
  • Microsoft Poland
Bibliografia
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  • Martzoukou, K., Fulton, C., Kostagiolas, P., & Lavranos, C. (2020). A study of higher education students' self- perceived digital competences for learning and everyday life online participation. Journal of Documentation, 76(6), 1413-1458. https://doi.org/10.1108/JD-03-2020-0041
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  • Oppenlaender, J. (2022). A Taxonomy of Prompt Modifiers for Text-to-Image Generation. arXiv preprint arXiv:2204.13988. https://doi.org/10.48550/arXiv.2204.13988
  • Oppenlaender, J., Linder, R., & Silvennoinen, J. (2023). Prompting AI Art: An Investigation into the Creative Skill of Prompt Engineering. arXiv preprint arXiv:2303.13534. https://doi.org/10.48550/arXiv.2303.13534
  • Oppenlaender, J., Visuri, A., Paananen, V., Linder, R., & Silvennoinen, J. (2023). Text-to-Image Generation: Perceptions and Realities. arXiv preprint arXiv:2303.13530. https://doi.org/10.48550/arXiv.2303.13530
  • Parsons, G. (2023). How to use AI image prompts to generate art using DALL-E. Retrieved from https://create.microsoft.com/en-us/learn/articles/how-to-image-prompts-dall-e-ai on 10 May, 2023.
  • Polak, M.P., & Morgan, D. (2023). Extracting Accurate Materials Data from Research Papers with Conversational Language Models and Prompt Engineering--Example of ChatGPT. arXiv preprint arXiv:2303.05352. https://doi.org/10.48550/arXiv.2303.05352
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  • Pozo-Sánchez, S., López-Belmonte, J., Rodríguez-García, A.M., & López-Núñez, J.A. (2020). Teachers' digital competence in using and analytically managing information in flipped learning (Competencia digital docente para el uso y gestión analítica informacional del aprendizaje invertido). Culture and Education, 32(2), 213- 241. https://doi.org/10.1080/11356405.2020.1741876
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  • Reynolds, L., & McDonell, K. (2021). Prompt programming for large language models: Beyond the few-shot paradigm. Paper presented at the Extended Abstracts of the 2021 CHI Conference on Human Factors in Computing Systems. https://doi.org/10.1145/3560815
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  • Wang, J., Shi, E., Yu, S., Wu, Z., Ma, C., Dai, H., Yang, Q., Kang, Y., Wu, J., Hu, H., Yue, C., Zhang, H., Liu, Y., Li, X., Ge, B. Zhu, D. Yuan, Y. Shen, D., Liu, T. & Zhang, S. (2023). Prompt Engineering for Healthcare: Methodologies and Applications. arXiv preprint arXiv:2304.14670. https://doi.org/10.48550/arXiv.2304.14670
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  • White, J., Fu, Q., Hays, S., Sandborn, M., Olea, C., Gilbert, H., Elnasharx, A., Spencer-Smith, J., & Schmidt, D.C. (2023). A prompt pattern catalog to enhance prompt engineering with chatgpt. arXiv preprint arXiv:2302.11382. https://doi.org/10.48550/arXiv.2302.11382
  • Zamfirescu-Pereira, J., Wong, R.Y., Hartmann, B., & Yang, Q. (2023). Why Johnny can't prompt: how non-AI experts try (and fail) to design LLM prompts. Paper presented at the Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems. https://doi.org/10.1145/3544548.3581388
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Typ dokumentu
Bibliografia
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Identyfikator YADDA
bwmeta1.element.ekon-element-000171673156

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