Tell Me Your Prompts and I Will Make Them True: The Alchemy of Prompt Engineering and Generative AI

Aras Bozkurt

doi:10.55982/openpraxis.16.2.661

Tell Me Your Prompts and I Will Make Them True: The Alchemy of Prompt Engineering and Generative AI

Open Praxis

Volume 16 (2024): Issue 2

By: Aras Bozkurt

Open Access

|Apr 2024

Abstract

This paper explores the emerging field of prompt engineering within generative AI, emphasizing its role as a critical intersection between art and science. Prompt engineering is identified as the key to unlocking the full potential of generative AI technologies by optimizing human-AI communication. Through a comprehensive analysis of the related literature, this study illustrates how prompt engineering transcends mere technical manipulation, requiring a blend of creativity, strategic thinking, and a deep understanding of generative AI capabilities. This paper provides various strategies for crafting effective prompts, from simple to sophisticated techniques, highlighting the importance of ethical considerations and the potential risks associated with prompt manipulation. By establishing a set of principles and guidelines, this paper aims to advance prompt engineering as a discipline essential for enhancing AI’s functionality and reliability and, with this justification, introduces the ‘Prompt Engineering for Gen[i]erative AI Framework’. After all, this paper calls for a multidisciplinary approach to prompt engineering, advocating for its recognition and development as a pivotal component of AI literacy and application. Through this exploration, this paper intends to contribute to the evolving dialogue on the integration of human creativity with generative AI capabilities, offering insights into the future of effective and ethical AI interaction.

References

Ansari, A. N., Ahmad, S., & Bhutta, S. M. (2023). Mapping the global evidence around the use of ChatGPT in higher education: A systematic scoping review. Education and Information Technologies, 1–41. DOI: 10.1007/s10639-023-12223-4
Open DOI Search in Google Scholar Back to article
Bozkurt, A. (2023a). Generative artificial intelligence (AI) powered conversational educational agents: The inevitable paradigm shift. Asian Journal of Distance Education, 18(1), 198–204. DOI: 10.5281/zenodo.7716416
Open DOI Search in Google Scholar Back to article
Bozkurt, A. (2023b). Unleashing the potential of generative AI, conversational agents and chatbots in educational praxis: A systematic review and bibliometric analysis of GenAI in education. Open Praxis, 15(4), 261–270. DOI: 10.55982/openpraxis.15.4.609
Open DOI Search in Google Scholar Back to article
Bozkurt, A. (2023c). Generative AI, synthetic contents, open educational resources (OER), and open educational practices (OEP): A new front in the openness landscape. Open Praxis, 15(3), 78–184. DOI: 10.55982/openpraxis.15.3.579
Open DOI Search in Google Scholar Back to article
Bozkurt, A. (2024). GenAI et al.: Cocreation, authorship, ownership, academic ethics and integrity in a time of generative AI. Open Praxis, 16(1). DOI: 10.55982/openpraxis.16.1.654
Open DOI Search in Google Scholar Back to article
Bozkurt, A., & Sharma, R. C. (2023). Generative AI and prompt engineering: The art of whispering to let the genie out of the algorithmic world. Asian Journal of Distance Education, 18(2), i–vii. DOI: 10.5281/zenodo.8174941
Open DOI Search in Google Scholar Back to article
Bozkurt, A., Xiao, J., Lambert, S., Pazurek, A., Crompton, H., Koseoglu, S., Farrow, R., Bond, M., Nerantzi, C., Honeychurch, S., Bali, M., Dron, J., Mir, K., Stewart, B., Costello, E., Mason, J., Stracke, C. M., Romero-Hall, E., Koutropoulos, A., Toquero, C. M., Singh, L., Tlili, A., Lee, K., Nichols, M., Ossiannilsson, E., Brown, M., Irvine, V., Raffaghelli, J. E., Santos-Hermosa, G., Farrell, O., Adam, T., Thong, Y. L., Sani-Bozkurt, S., Sharma, R. C., Hrastinski, S., & Jandrić, P. (2023). Speculative futures on ChatGPT and generative artificial intelligence (AI): A collective reflection from the educational landscape. Asian Journal of Distance Education, 18(1), 53–130. DOI: 10.5281/zenodo.7636568
Open DOI Search in Google Scholar Back to article
Bsharat, S. M., Myrzakhan, A., & Shen, Z. (2023). Principled Instructions Are All You Need for Questioning LLaMA-1/2, GPT-3.5/4. arXiv. DOI: 10.48550/arXiv.2312.16171
Open DOI Search in Google Scholar Back to article
Chen, B., Zhang, Z., Langrené, N., & Zhu, S. (2023). Unleashing the potential of prompt engineering in Large Language Models: a comprehensive review. arXiv. DOI: 10.48550/arXiv.2310.14735
Open DOI Search in Google Scholar Back to article
Dang, H., Mecke, L., Lehmann, F., Goller, S., & Buschek, D. (2022). How to prompt? Opportunities and challenges of zero-and few-shot learning for human-AI interaction in creative applications of generative models. arXiv. DOI: 10.48550/arXiv.2209.01390
Open DOI Search in Google Scholar Back to article
Descartes, R. (1637). Discourse on the Method of Rightly Conducting One’s Reason and of Seeking Truth in the Sciences. Project Gutenberg. https://www.gutenberg.org/files/59/59-h/59-h.htm
Search in Google Scholar Back to article
Diao, S., Wang, P., Lin, Y., & Zhang, T. (2023). Active prompting with chain-of-thought for large language models. arXiv. DOI: 10.48550/arXiv.2302.12246
Open DOI Search in Google Scholar Back to article
Dwivedi, Y. K., Kshetri, N., Hughes, L., Slade, E. L., Jeyaraj, A., Kar, A. K., Baabdullah, A. M., Koohang, A., Raghavan, V., Ahuja, M., Albanna, H., Albashrawi, M. A., Al-Busaidi, A. S., Balakrishnan, J., Barlette, Y., Basu, S., Bose, I., Brooks, L., Buhalis, D., … Wright, R. (2023). Opinion Paper: “So what if ChatGPT wrote it?” Multidisciplinary perspectives on opportunities, challenges and implications of generative conversational AI for research, practice and policy. International Journal of Information Management, 71, 102642. DOI: 10.1016/j.ijinfomgt.2023.102642
Open DOI Search in Google Scholar Back to article
Gates, B. (2023). The Age of AI has begun. Gates Notes. https://www.gatesnotes.com/The-Age-of-AI-Has-Begun
Search in Google Scholar Back to article
Johnson, W. L. (2023). How to Harness Generative AI to Accelerate Human Learning. International Journal of Artificial Intelligence in Education, 1–5. DOI: 10.1007/s40593-023-00367-w
Open DOI Search in Google Scholar Back to article
Kakun, A., & Tytenko, S. (2023). Generative AI And Prompt Engineering in Education. Modern Engineering and Innovative Technologies, 1(29-01), 117–121. DOI: 10.30890/2567-5273.2023-29-01-052
Open DOI Search in Google Scholar Back to article
Lewis, P., Perez, E., Piktus, A., Petroni, F., Karpukhin, V., Goyal, N., Kuttler, H., Lewis, M., Yih, W., Rocktäschel, T., Riedel, S., & Kiela, D. (2020). Retrieval-augmented generation for knowledge-intensive nlp tasks. Advances in Neural Information Processing Systems, 33, 9459–9474. https://dl.acm.org/doi/abs/10.5555/3495724.3496517
Search in Google Scholar Back to article
Li, Z., Peng, B., He, P., Galley, M., Gao, J., & Yan, X. (2023). Guiding Large Language Models via Directional Stimulus Prompting. arXiv. DOI: 10.48550/arXiv.2302.11520
Open DOI Search in Google Scholar Back to article
Liu, Y., Deng, G., Xu, Z., Li, Y., Zheng, Y., Zhang, Y., Zhao, L., Zhang, T., & Liu, Y. (2023c). Jailbreaking chatGPT via prompt engineering: An empirical study. arXiv. DOI: 10.48550/arXiv.2305.13860
Open DOI Search in Google Scholar Back to article
Liu, P., Yuan, W., Fu, J., Jiang, Z., Hayashi, H., & Neubig, G. (2023a). Pre-train, prompt, and predict: A systematic survey of prompting methods in natural language processing. ACM Computing Surveys, 55(9), 1–35. DOI: 10.1145/3560815
Open DOI Search in Google Scholar Back to article
Liu, Z., Yu, X., Fang, Y., & Zhang, X. (2023b). Graphprompt: Unifying pre-training and downstream tasks for graph neural networks. In Proceedings of the ACM Web Conference 2023 (pp. 417–428). 30 April 2023–4 May 2023, Austin, TX, USA. DOI: 10.1145/3543507.3583386
Open DOI Search in Google Scholar Back to article
Lo, L. S. (2023a). The CLEAR path: A framework for enhancing information literacy through prompt engineering. The Journal of Academic Librarianship, 49(4), 102720. DOI: 10.1016/j.acalib.2023.102720
Open DOI Search in Google Scholar Back to article
Lo, L. S. (2023b). The Art and Science of Prompt Engineering: A New Literacy in the Information Age. Internet Reference Services Quarterly, 27(4), 203–210. DOI: 10.1080/10875301.2023.2227621
Open DOI Search in Google Scholar Back to article
McGuire, A. (2023). Leveraging ChatGPT for Rethinking Plagiarism, Digital Literacy, and the Ethics of Co-Authorship in Higher Education: A Position Paper and Comparative Critical Reflection of Composing Processes. Irish Journal of Technology Enhanced Learning, 7(2), 21–31. DOI: 10.22554/ijtel.v7i2.131
Open DOI Search in Google Scholar Back to article
Merriam-Webster. (2024a). Art. Merriam-Webster.com dictionary. https://www.merriam-webster.com/dictionary/art
Search in Google Scholar Back to article
Merriam-Webster. (2024b). Science. Merriam-Webster.com dictionary. https://www.merriam-webster.com/dictionary/science
Search in Google Scholar Back to article
O’Connor, S., Peltonen, L.-M., Topaz, M., Chen, L.-Y. A., Michalowski, M., Ronquillo, C., Stiglic, G., Chu, C. H., Hui, V., & Denis-Lalonde, D. (2024). Prompt engineering when using generative AI in nursing education. Nurse Education in Practice, 74, 103825. DOI: 10.1016/j.nepr.2023.103825
Open DOI Search in Google Scholar Back to article
OpenAI. (2022). Introducing ChatGPT. https://openai.com/blog/chatgpt
Search in Google Scholar Back to article
OpenAI. (2023a). ChatGPT can now see, hear, and speak. https://openai.com/blog/chatgpt-can-now-see-hear-and-speak
Search in Google Scholar Back to article
OpenAI. (2023b). Prompt engineering. https://platform.openai.com/docs/guides/prompt-engineering
Search in Google Scholar Back to article
Paranjape, B., Lundberg, S., Singh, S., Hajishirzi, H., Zettlemoyer, L., & Ribeiro, M. T. (2023). ART: Automatic multi-step reasoning and tool-use for large language models. arXiv. DOI: 10.48550/arXiv.2303.09014
Open DOI Search in Google Scholar Back to article
Sarı, T., Nayir, F., & Bozkurt, A. (2024). Reimagining education: Bridging artificial intelligence, transhumanism, and critical pedagogy. Journal of Educational Technology and Online Learning, 7(1), 102–115. DOI: 10.31681/jetol.1308022
Open DOI Search in Google Scholar Back to article
Sharma, R. C., & Bozkurt, A. (2024). Transforming Education With Generative AI: Prompt Engineering and Synthetic Content Creation. IGI Global. DOI: 10.4018/979-8-3693-1351-0
Open DOI Search in Google Scholar Back to article
Velásquez-Henao, J. D., Franco-Cardona, C. J., & Cadavid-Higuita, L. (2023). Prompt Engineering: a methodology for optimizing interactions with AI-Language Models in the field of engineering. DYNA, 90(230), 9–17. DOI: 10.15446/dyna.v90n230.111700
Open DOI Search in Google Scholar Back to article
Wei, J., Wang, X., Schuurmans, D., Bosma, M., Chi, E. H., Xia, F., Le, Q., & Zhou, D. (2022). Chain of Thought Prompting Elicits Reasoning in Large Language Models. In Advances in Neural Information Processing Systems, 35, 24824–24837. https://proceedings.neurips.cc/paper_files/paper/2022/hash/9d5609613524ecf4f15af0f7b31abca4-Abstract-Conference.html
Search in Google Scholar Back to article
White, J., Fu, Q., Hays, S., Sandborn, M., Olea, C., Gilbert, H., Elnashar, A., Spencer-Smith, J., & Schmidt, D. C. (2023). A Prompt Pattern Catalog to Enhance Prompt Engineering with ChatGPT. arXiv. DOI: 10.48550/arXiv.2302.11382
Open DOI Search in Google Scholar Back to article
Yao, S., Yu, D., Zhao, J., Shafran, I., Griffiths, T. L., Cao, Y., & Narasimhan, K. (2023). Tree of thoughts: Deliberate problem solving with large language models. arXiv. DOI: 10.48550/arXiv.2305.10601
Open DOI Search in Google Scholar Back to article
Yao, S., Zhao, J., Yu, D., Du, N., Shafran, I., Narasimhan, K., & Cao, Y. (2022). React: Synergizing reasoning and acting in language models. arXiv. DOI: 10.48550/arXiv.2210.03629
Open DOI Search in Google Scholar Back to article
Yong, G., Jeon, K., Gil, D., & Lee, G. (2023). Prompt engineering for zero-shot and few-shot defect detection and classification using a visual-language pretrained model. Computer-Aided Civil and Infrastructure Engineering, 38(11), 1536–1554. DOI: 10.1111/mice.12954
Open DOI Search in Google Scholar Back to article
Zhang, Z., Zhang, A., Li, M., Zhao, H., Karypis, G., & Smola, A. (2023). Multimodal chain-of-thought reasoning in language models. arXiv. DOI: 10.48550/arXiv.2302.00923
Open DOI Search in Google Scholar Back to article
Zhou, D., Scharli, N., Hou, L., Wei, J., Scales, N., Wang, X., Schuurmans, D., Bousquet, O., Le, Q., & Chi, E. H. (2022). Least-to-Most Prompting Enables Complex Reasoning in Large Language Models. arXiv. DOI: 10.48550/arXiv.2205.10625
Open DOI Search in Google Scholar Back to article

Articles in this issue

DOI: https://doi.org/10.55982/openpraxis.16.2.661 | Journal eISSN: 2304-070X

Journal RSS Feed

Language: English

Page range: 111 - 118

Submitted on: Jan 30, 2024

Accepted on: Feb 27, 2024

Published on: Apr 3, 2024

Published by: International Council for Open and Distance Education (ICDE)

In partnership with: Paradigm Publishing Services

Keywords:

prompt engineering,

cocreation,

human-AI interaction,

generative AI,

GenAI,

artificial intelligence,

AI,

AIEd,

education,

higher education,

educational technology,

generative pre-trained transformer,

GPT,

ChatGPT,

large language models,

LLMs,

natural language processing,

NLP

© 2024 Aras Bozkurt, published by International Council for Open and Distance Education (ICDE)
This work is licensed under the Creative Commons Attribution 4.0 License.

Volume 16 (2024): Issue 2