İş İnovasyonu için Stratejik Hızlı Mühendislik: Sektörler Arasında Yapay Zekanın Gücünü Ortaya Çıkarmak

Öz

Bu çalışma, çeşitli sektörlerde yapay zeka (AI) uygulamalarının etkinliğini artırmada prompt mühendisliğinin dönüştürücü rolünü araştırmaktadır. AI teknolojileri, özellikle büyük dil modelleri (LLM'ler), organizasyonel operasyonlara giderek daha fazla entegre oldukça, bu modelleri yönlendirmek için kullanılan promptların kalitesi, bunların yararlılığını belirleyen kritik bir faktör olarak ortaya çıkmıştır. Prompt mühendisliği, AI sistemlerinin doğru, alakalı ve bağlamsal olarak uygun çıktılar üretmesini yönlendiren girdi talimatlarının stratejik olarak oluşturulmasını ifade eder. Çalışma, promptların tasarımını ve yapısını inceleyerek, kuruluşların karar alma süreçlerini, müşteri deneyimlerini, bilgi üretimini ve operasyonel verimliliği nasıl önemli ölçüde iyileştirebileceklerini göstermektedir. Perakende, sağlık, eğitim, finans ve imalat gibi sektörlerden elde edilen ampirik kanıtlar ve sektörler arası vaka çalışmalarına dayanan araştırma, iyi formüle edilmiş promptların ve kötü yapılandırılmış promptların etkilerini karşılaştırmaktadır. Bulgular, etkili komutların yalnızca AI modellerinin teknik performansını optimize etmekle kalmayıp, aynı zamanda inovasyonu teşvik ettiğini, kaynak israfını azalttığını ve uzun vadeli rekabet gücünü desteklediğini ortaya koymaktadır. Ayrıca, makale sektörlere özgü komut stratejileri geliştirmek için pratik öneriler sunmakta ve çalışanlara komut okuryazarlığı konusunda eğitim vermenin önemini vurgulamaktadır. Sonuç olarak, çalışma komut mühendisliğini, insan-AI işbirliğinin gelişen ortamında temel bir beceri olarak konumlandırmakta ve AI'nın tüm potansiyelini sorumlu ve stratejik bir şekilde kullanmak için gerekli olduğunu belirtmektedir.

Anahtar Kelimeler

• İstem mühendisliği
• Yapay zeka
• İş inovasyonu
• Stratejik tasarım
• Sektöre özel yapay zeka kullanımı.

Strategic Prompt Engineering for Business Innovation: Unlocking the Power of AI Across Industries

Öz

This study explores the transformative role of prompt engineering in enhancing the effectiveness of artificial intelligence (AI) applications across diverse industries. As AI technologies, particularly large language models (LLMs), become increasingly integrated into organizational operations, the quality of the prompts used to guide these models has emerged as a critical factor in determining their usefulness. Prompt engineering refers to the strategic construction of input instructions that guide AI systems to generate accurate, relevant, and contextually appropriate outputs.By examining the design and structure of prompts, this article demonstrates how organizations can significantly improve decision-making processes, customer experiences, knowledge generation, and operational efficiency. Drawing on empirical evidence and cross-sector case studies from industries such as retail, healthcare, education, finance, and manufacturing, the research contrasts the impacts of well-formulated versus poorly constructed prompts. The findings reveal that effective prompts not only optimize the technical performance of AI models but also foster innovation, reduce resource waste, and support long-term competitiveness. Furthermore, the article offers practical recommendations for developing industry-specific prompt strategies and emphasizes the importance of training employees in prompt literacy. Ultimately, the study positions prompt engineering as a foundational skill in the evolving landscape of human-AI collaboration, essential for leveraging AI’s full potential in a responsible and strategic manner.

Anahtar Kelimeler

• Prompt engineering
• Artificial intelligence
• Business innovation
• Strategic design
• Industry-specific AI use.

Kaynakça

1. Arya, G., Hasan, M. K., Bagwari, A., Safie, N., Islam, S., Ahmed, F. R. A, De, A. & Ghazal, T. H. (2023). Multimodal hate speech detection in memes using contrastive language-image pre-training. IEEE Access, 12, 22359-22375. https://doi.org/10.1109/ACCESS.2024.3361322
2. Argote, L. & Miron-Spektor, E. (2011). Organizational learning: From experience to knowledge. Organization Science, 22(5), 1123–1137. https://doi.org/10.1287/orsc.1100.0621
3. Bao, S., Li, T. & Cao, B. (2024). Chain-of-event prompting for multi-document summarization by large language models. International Journal of Web Information Systems, 20 (3), 229-247. https://doi.org/10.1108/IJWIS-12-2023-0249
4. Birol, E. (2023). Türkiye’de en çok ziyaret edilen pazaryeri web sitelerinin görsel tasarım süreci. İNİF E- Dergi, 8(1), 107-131.
5. Bozkurt, A. (2023a). Generative ai, synthetic contents, open educational eesources (OER), and open educational practices (OEP): A new front in the openness landscape. Open Praxis, 15(3), 178–184. https://doi.org/10.55982/openpraxis.15.3.579
6. 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. https://doi.org/10.55982/openpraxis.15.4.609
7. Cain, W. (2024). Prompting Change: Exploring Prompt Engineering in Large Language Model AI and Its Potential to Transform Education. TechTrends, 68, 47–57. https://doi.org/10.1007/s11528-023-00896-0
8. Cheung, K. S. (2024). Real Estate Insights Unleashing the potential of ChatGPT in property valuation reports: the “Red Book” compliance Chain-of-thought (CoT) prompt engineering. Journal of Property Investment & Finance, 42(2), 200-206. https://doi.org/10.1108/JPIF-06 2023-0053

9. Chubb, L. A. (2023). Me and the Machines: Possibilities and Pitfalls of Using Artificial Intelligence for Qualitative Data Analysis. International Journal of Qualitative Methods, 22, 116. https://doi.org/10.1177/16094069231193593
10. Clavie, B., Soulie, G., Naylor, F. & Brightwell, T. (2023). Towards Simple Hybrid Language Model Reasoning Through Human Explanations Enhanced Prompts. HHAI 2023: Augmenting Human Intellect, 379-381. http://dx.doi.org/10.3233/FAIA230103
11. Davenport, T. H. (1993). Process Innovation: Reengineering Work Through Information Technology. Harvard Business School Press.
12. Davenport, T. H. & Spanyi, A. (2019). Digital process transformation. MIT Sloan Management Review, https://sloanreview.mit.edu/article/digital-transformation-should-start-with-customers/
13. Du, Y., Wei, F., Zhang, Z., Shi, M., Gao, Y.& Li, G. (2022). Learning to Prompt for Open Vocabulary Object Detection with Vision-Language Model. IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 14064-14073. https://doi.org/10.1109/CVPR52688.2022.01369
14. Fernandez, A. S. & Cornell, K. A. (2024). CS1 with a Side of AI: Teaching Software Verification for Secure Code in the Era of Generative AI. 55th ACM Technical Symposium on Computer Science Education, 345–351. https://doi.org/10.1145/3626252.3630817
15. Gao, Y., Nuchged, B., Li, Y. & Peng, L. (2024). An Investigation of Applying Large Language Models to Spoken Language Learning. Applied Sciences, 14, 1-18. https://doi.org/10.3390/app14010224
16. Giray, L. (2023). Prompt Engineering with ChatGPT: A Guide for Academic Writers. Annals of Biomedical Engineering, 51, 2629–2633. https://doi.org/10.1007/s10439-023-03272-4
17. Goloujeh, A. M., Sullivan, A. & Magerko, B. (2024). Is It AI or Is It Me? Understanding Users’ Prompt Journey with Text-to-Image Generative AI Tools. In Proceedings of the CHI Conference on Human Factors in Computing Systems (CHI '24). Association for Computing Machinery, New York, NY, USA, 1–13. https://doi.org/10.1145/3613904.364286
18. Görer, B. & Aydemir, F. B. (2023). Generating Requirements Elicitation Interview Scripts with Large Language Models. 31st International Requirements Engineering Conference Workshops (REW), 44-51. https://doi.org/10.1109/REW57809.2023.00015
19. Hall, B. & McKee, J. (2024). An early or somewhat late ChatGPT guide for librarians, Journal of Business & Finance Librarianship, 29(1), 58-69. https://doi.org/10.1080/08963568.2024.2303944
20. Hammer, M. & Champy, J. (1993). Reengineering the Corporation: A Manifesto for Business Revolution. HarperBusiness.
21. Heston, T. F. & Khun, C. (2023). Prompt Engineering in Medical Education. International Medical Education, 2, 198-205. https://doi.org/10.3390/ime2030019
22. Huang, M. H. & Rust, R. T. (2024). The Caring Machine: Feeling AI for Customer Care. Journal of Marketing, 1-23. https://doi.org/10.1177/00222429231224748
23. Jin, J. & Kim, M. (2024). GPT-Empowered Personalized eLearning System for Programming Languages. Applied Sciences, 13, 1-27. https://doi.org/10.3390/app132312773
24. Jo, H., Lee, J. K., Lee Y. C. & Choo, S. (2024). Gener a ti v e artificial intelligence and building design: early photorealistic render visualization of façades using local identity-trained models. Journal of Computational Design and Engineering, 11, 85-105. https://doi.org/10.1093/jcde/qwae017
25. Kim, S., Eun, J., Oh, C. & Lee, J. (2024). “Journey of Finding the Best Query”: Understanding the User Experience of AI Image Generation System. International Journal of Human Computer Interaction, 1-20. https://doi.org/10.1080/10447318.2024.2307670
26. Kshetri, N. (2023). Generative Artificial Intelligence and the Economics of Effective Prompting. IEEE Computer Society, 112-118.
27. Lee, G. H., Lee, K. Y., Jeong, B. & Kim, T. (2024). Developing Personalized Marketing Service Using Generative AI. IEEEAccess, 12, 22394-22402. https://doi.org/10.1109/ACCESS.2024.3361946
28. Lee, U., Jung, H., Jeon, Y., Sohn, Y., Hwang, W., Moon, J. & Kim, H. (2023). Few‑shot is enough: exploring ChatGPT prompt engineering method for automatic question generation in English education. Education and Information Technologies, 1-33. https://doi.org/10.1007/s10639-023-12249-8
29. Lim, S. & Schmälzle, R. (2023). Artificial intelligence for health message generation: an empirical study using a large language model (LLM) and prompt engineering. Frontiers in Communication, 8, 1-15. https://doi.org/10.3389/fcomm.2023.112908 2
30. Liu, M., Wang, J., Lin, T., Ma, Q., Fang, Z. & Wu, Y. (2024). An Empirical Study of the Code Generation of Safety-Critical Software Using LLMs. Applied Sciences, 14, 1-41. https://doi.org/10.3390/app14031046
31. Liu, V. & Chilton, L. B. (2022). Design guidelines for prompt engineering text-to-image generative models. In: Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems, 1-23. https://doi.org/10.1145/3491102.3501825
32. Lo, L. S. (2023). The CLEAR path: A framework for enhancing information literacy throug hprompt engineering. The Journal of Academic Librarianship, 49, 1-3. https://doi.org/10.1016/j.acalib.2023.102720
33. Magahed, F. M., Chen, Y., Ferris, J. A., Knoth, S. & Jones-Farmer, L. A. (2024). How generative AI models such as ChatGPT can be (mis)used in SPC practice, education, and research? An exploratory study, Quality Engineering, 36(2), 287-315. https://doi.org/10.1080/08982112.2023.2206479
34. Miao, J., Thongprayoon, C., Suppadungsuk, S., Valencia, O. A. G. & Cheungpasitporn, W. (2024). Integrating Retrieval-Augmented Generation with Large Language Models in Nephrology: Advancing Practical Applications. Medicana, 60, 445-460. https://doi.org/10.3390/medicina60030445
35. Nambisan, S., Lyytinen, K., Majchrzak, A. & Song, M. (2017). Digital innovation management: Reinventing innovation management research in a digital world. MIS Quarterly, 41(1), 223–238. https://doi.org/10.25300/MISQ/2017/41:1.03
36. Nguyen, J. & Pepping, C. A. (2023). The application of ChatGPT in healthcare progress notes: A commentary from a clinical and research perspective. Clinical and Translational Medicine, 13, 1-3. https://doi.org/10.1002/ctm2.1324
37. Papa, L., Faiella, L., Corvitto, L. Maiano, L. & Amerini, I. (2023). On the use of Stable Diffusion for creating realistic faces: from generation to detection. 11th International Workshop on Biometrics and Forensics (IWBF), 1-6. https://doi.org/10.1109/IWBF57495.2023.10156981
38. Polverini, G. & Gregorcic, B. (2024). How understanding large language models can inform the use of ChatGPT in physics education. European Journal of Physics, 45, 1-35. https://doi.org/10.1088/1361-6404/ad1420
39. Park, D., An, G., Kamyod, C. & Kim, C., G. (2024). A Study on Performance Improvement of Prompt Engineering for Generative AI with a Large Language Model. Journal of Web Engineering, 22(8), 1187-1206. https://doi.org/10.13052/jwe1540-9589.2285
40. Patton, M. Q. (2002). Qualitative Research & Evaluation Methods (3rd ed.). Sage Publications.
41. Pu, Z., Jia, B. & Shi, C. (2024). ChatGPT and generative AI are revolutionizing the scientific community: A Janus‐faced conundrum. iMeta, 1-7. https://doi.org/10.1002/imt2.178
42. Putra, G. S. E. & Labasariyani, N. L. P. (2024). Medical records information system based on prototyping model using automatic identifier NFC card. World Journal of Advanced Engineering Technology and Sciences, 11(1), 1-13. https://doi.org/10.30574/wjaets.2024.11.1.0315
43. Sariyildiz, M. B., Alahari, K., Larlus, D. & Kalantidis, Y. (2023). Fake it till you make it: Learning transferable representations from synthetic ImageNet clones. IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). https://doi.org/10.1109/CVPR52729.2023.00774
44. Scoccia, G. L. (2023). Exploring Early Adopters’ Perceptions of ChatGPT as a Code Generation Tool. 38th IEEE/ACM International Conference on Automated Software Engineering Workshops (ASEW), 88-63. https://doi.org/10.1109/ASEW60602.2023.00016
45. Shen, Y., Ai, X., Raj, A. G. S., John, R. J. L. & Syamkumar, M. (2024). Implications of ChatGPT for Data Science Education. Implications of ChatGPT for Data Science Education. In Proceedings of the 55th ACM Technical Symposium on Computer Science Education V. 1 (SIGCSE 2024) , Portland, OR, USA. https://doi.org/10.1145/3626252.3630874
46. Shi, Y., Ren, P., Wang, J., Han, B., Aslani, T., Agbavor, F., Zhang, Y., Hu, M., Zhao, L. & Liang, F. (2023). Leveraging GPT-4 for food effect summarization to enhance product-specific guidance development via iterative prompting. Journal of Biomedical Informatics, 148, 1-9. https://doi.org/10.1016/j.jbi.2023.104533
47. Shin, E. & Ramanathan, M. (2024). Evaluation of prompt engineering strategies for pharmacokinetic data analysis with the ChatGPT large language model. Journal of Pharmacokinetics and Pharmacodynamics, 51, 101–108. https://doi.org/10.1007/s10928-023 09892-6
48. Spasić, A. J. & Janković, D. S. (2023). Using ChatGPT Standard Prompt Engineering Techniques in Lesson Preparation: Role, Instructions and Seed-Word Prompts. 58th International Scientific Conference on Information, Communication and Energy Systems and Technologies (ICEST), 1-4. https://doi.org/10.1109/ICEST58410.2023.10187269
49. Strobelt, H., Webson, A., Sanh, V., Hoover, B., Beyer, J., Pfister, H.&Rush, A. M. (2023). Interactive and Visual Prompt Engineering for Ad-hoc Task Adaptation with Large Language Models. IEEE Transaction on Visualization and Computer Graphics, 29(1), 1146-1156. https://doi.org/10.1109/TVCG.2022.3209479
50. Sukkar, A. W., Fareed, M., W., Yahia, M., W., Abdalla, S., B., Ibrahim, I. & Senjab, K., A., K. 2024). Analytical Evaluation ofMidjourney Architectural Virtual Lab: DefiningMajor Current Limits in AI-Generated Representations of Islamic Architectural Heritage. Buildings, 14, 1-25. https://doi.org/10.3390/buildings14030786
51. Tafesse, W. & Wien, A. (2024). ChatGPT’s applications in marketing: a topic modeling approach. Marketing Intelligence & Planning, https://doi.org/10.1108/MIP-10-2023-0526
52. Tafesse, W. & Wood, B. (2024). Hey ChatGPT: an examination of ChatGPT prompts in marketing. Journal of Marketing Analytics, 1-16. https://doi.org/10.1057/s41270-023-00284-w
53. Kleinig, O., Gao, C., Kovoor, J. , G., Gupta, A. K., Bacchi, S. & Chan, W. O. (2023). How to use large language models in ophthalmology: from prompt engineering to protecting confidentiality. Eye, 38, 649-653. https://doi.org/10.1038/s41433-023-02772-w
54. Takafoli, M., Li, S. & Mäkelä, V. (2024). Generative AI in User Experience Design and Research: How Do UX Practitioners, Teams, and Companies Use GenAI in Industry? In Proceedings of the 2024 ACM Designing Interactive Systems Conference (DIS '24). Association for Computing Machinery, New York, NY, USA, 1579–1593. https://doi.org/10.1145/3643834.3660720
55. Teece, D. J. (2007). Explicating dynamic capabilities: The nature and microfoundations of (sustainable) enterprise performance. Strategic Management Journal, 28(13), 1319–1350. https://doi.org/10.1002/smj.640 Teece, D. J. (2018). Business models and dynamic capabilities. Long Range Planning, 51(1), 40–49. https://doi.org/10.1016/j.lrp.2017.06.007
56. Teixeira, A. C., Marar, V., Yazdanpanah, H., Oliveira, A. & Ghassemi, M. (2023). Enhancing Credit Risk Reports Generation using LLMs: An Integration of Bayesian Networks and Labeled Guide Prompting. ICAIF '23: Proceedings of the Fourth ACM International Conference on AI in Finance, 340-38. https://doi.org/10.1145/3604237.3626902
57. Trad, F. & Chehab, A. (2024). Prompt Engineering or Fine-Tuning? A Case Study on Phishing Detection with Large Language Models. Machine Learning Knowladge Extraction, 6, 367-384. https://doi.org/10.3390/make6010018
58. Tupper, M., Hendy, I. W. & Shipway, J. R. (2024). Field courses for dummies: To what extent can ChatGPT design a higher education field course?. Innovations in Education and Teaching International, 1-16. https://doi.org/10.1080/14703297.2024.2316716
59. Vartianen, H. & Tedre, M. (2023). Using artificial intelligence in craft education: crafting with text-to-image generative models. Digital Creativity, 34(1), 1-21. https://doi.org/10.1080/14626268.2023.2174557
60. Vial, G. (2019). Understanding digital transformation: A review and a research agenda. Journal of Strategic Information Systems, 28(2), 118–144. https://doi.org/10.1016/j.jsis.2019.01.003
61. vom Brocke, J., Mendling, J. & Rosemann, M. (2021). Business Process Management Cases Vol. 2: Digital Transformation – Strategy, Processes and Execution. Springer. https://doi.org/10.1007/978-3-030-72970-2
62. Vermeersch, A. (2023). Deep learning for K3 fibrations in heterotic/Type IIA string duality. Nuclear Physics B, 993, 1-14. https://doi.org/10.1016/j.nuclphysb.2023.116279
63. Walter, Y. (2024). Embracing the future of Artificial Intelligence in the classroom: the relevance of AI literacy, prompt engineering, and critical thinking in modern education. International Journal of Educational Technology in Higher Education, 21-28. https://doi.org/10.1186/s41239-024-00448-3
64. Wang, J., Liu, Z., Zhao, L., Wu, Z., Ma, C., Yu, S., Dai, H., Yang, Q., Liu, Y., Zhang, S., Shi, E. Pan, Y., Zhang, T., Zhu, D., Li, X., Jiang, X., Ge, B., Yuan, Y., Shen, D., Liu, T. & Zhang, S. (2023). Review of large vision models and visual prompt engineering. Meta-Radiology, 1, 1 13. https://doi.org/10.1016/j.metrad.2023.100047
65. 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., Pan, Y., Liu, Z., Sun, L., Li, X., Ge, B., Jiang, X., Zhu, D., Yuan, Y., Shen, D., Liu, T. & Zhang, S. (2021). Prompt Engineering for Healthcare: Methodologies and Applications, Journal of Latex Class Files, 18(4), 1-18. https://doi.org/10.48550/arXiv.2304.14670 Wang, M., Wang, M., Xu, X., Yang, L., Cai, D. & Yin, M. (2024). Unleashing ChatGPT’s Power: A Case Study on Optimizing Information Retrieval in Flipped Classrooms via Prompt Engineering. Transactions on Learning Technologies, 17, 629-641. https://doi.org/10.1109/TLT.2023.3324714
66. Watson, R. (2024). Prompt engineering when using generative AI in nursing education. Nurse Education in Practice, 74, 1-3. https://doi.org/10.1016/j.nepr.2023.103825
67. Xie, J., Li, X., Yuan, Y., Guan, Y., Jiang, J., Guo, X. & Peng, X. (2024). Knowledge based dynamic prompt learning for multi-label disease diagnosis. Knowledge-Based Systems, 286, 1 10. https://doi.org/10.1016/j.knosys.2024.111395
68. Ye, Q., Axmed, M., Pryzant, R. & Khani, F. (2024). Prompt Engineering a Prompt Engineer. https://doi.org/10.48550/arXiv.2311.05661
69. Yong, B., Jeon, K. Gil, D. & Lee, G. (2022). 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, 1536-1554. https://doi.org/10.1111/mice.12954
70. Yin, R. K. (2018). Case Study Research and Applications: Design and Methods (6th ed.). Sage Publications. Zamfirescu-Pereira, J. D., Wong, R. Y., Hartmann, B. & Yang, O. (2023). Why Johnny Can’t Prompt: How Non-AI Experts Try (and Fail) to Design LLM Prompts. Human Factors in Computing Systems, 1-21. https://doi.org/10.1145/3544548.3581388
71. Zhang, K., Zhou, F., Wu, L., Xie, N. & He, Z. (2024). Semantic understanding and prompt engineering for large-scale traffic data imputation. Information Fusion, 102, 1-17. https://doi.org/10.1016/j.inffus.2023.102038
72. Zheng, J. & Fischer, M. (2023). Dynamic prompt-based virtual assistant framework for BIM information search. Automation in Construction, 155, 1-24. https://doi.org/10.1016/j.autcon.2023.105067