Prompt Adaptation as a Dynamic Complement in Generative AI Systems

References

• Arthur WB (2009) The Nature of Technology: What It Is and How It Evolves (The Free Press, New York).Google Scholar
• Attewell P (1992) Technology diffusion and organizational learning: The case of business computing. Organ. Sci. 3(1):1–19.Link, Google Scholar
• Bharadwaj A (2000) A resource-based perspective on information technology capability and firm performance: An empirical investigation. MIS Quart. 24(1):169–196.Crossref, Google Scholar
• Bick A, Blandin A, Deming DJ (2026) The rapid adoption of generative AI. Management Sci., ePub ahead of print January 20, https://doi.org/10.1287/mnsc.2025.02523.Google Scholar
• Böhm K, Schedlberger L (2023) The use of generative AI in the domain of human creations—A case for co-evolution? Proc. 9th Internat. Conf. Socio-Technical Perspect. IS (STPIS’23) (CEUR Workshop Proceedings, Portsmouth, UK).Google Scholar
• Boiko D, MacKnight R, Kline B, Gomes G (2023) Autonomous chemical research with large language models. Nature 624(7992):570–578.Crossref, Google Scholar
• Bradley RA, Terry ME (1952) Rank analysis of incomplete block designs: I. The method of paired comparisons. Biometrika 39(3/4):324–345.Crossref, Google Scholar
• Bright J, Enock FE, Esnaashari S, Francis J, Hashem Y, Morgan D (2025) Generative AI is already widespread in the public sector: Evidence from a survey of UK public sector professionals. Digit. Gov.: Res. Pract. 6(1), Article 2, 13.Google Scholar
• Brynjolfsson E (1993) The productivity paradox of information technology. Comm. ACM 36(12):66–77.Crossref, Google Scholar
• Brynjolfsson E, Hitt LM (2000) Beyond computation: Information technology, organizational transformation and business performance. J. Econom. Perspect. 14(4):23–48.Crossref, Google Scholar
• Brynjolfsson E, Li D, Raymond LR (2025) Generative AI at work. Quart. J. Econom. 140(2):889–942.Google Scholar
• Brynjolfsson E, Rock D, Syverson C (2021) The productivity j-curve: How intangibles complement general purpose technologies. Amer. Econom. J. Macroeconom. 13(1):333–372.Crossref, Google Scholar
• Chen L, Zaharia M, Zou J (2024) How is ChatGPT’s behavior changing over time? Harvard Data Sci. Rev. 6(2).Google Scholar
• David PA (1990) The dynamo and the computer: An historical perspective on the modern productivity paradox. Am. Econ. Rev. 80(2):355–361.Google Scholar
• Dell’Acqua F, McFowland E, Mollick ER, Lifshitz-Assaf H, Kellogg K, Rajendran S, Krayer L, Candelon F, Lakhani KR (2026) Navigating the jagged technological frontier: Field experimental evidence of the effects of artificial intelligence on knowledge worker productivity and quality. Organ. Sci. 37(2):403–423.Google Scholar
• Don-Yehiya S, Choshen L, Abend O (2023) Human learning by model feedback: The dynamics of iterative prompting with midjourney. Bouamor H, Pino J, Bali K, eds. Proc. 2023 Conf. Empirical Methods Natural Language Processing (Association for Computational Linguistics, Stroudsburg, PA), 4146–4161.Google Scholar
• Dosi G (1982) Technological paradigms and technological trajectories: A suggested interpretation of the determinants and directions of technical change. Res. Policy 11(3):147–162.Crossref, Google Scholar
• Ford LR (1957) Solution of a ranking problem from binary comparisons. Amer. Math. Monthly 64(8P2):28–33.Crossref, Google Scholar
• Fu S, Tamir N, Sundaram S, Chai L, Zhang R, Dekel T, Isola P (2023) DreamSim: Learning new dimensions of human visual similarity using synthetic data. Preprint, submitted June 15, https://arxiv.org/abs/2306.09344.Google Scholar
• Fügener A, Grahl J, Gupta A, Ketter W (2022) Cognitive challenges in human–artificial intelligence collaboration: Investigating the path toward productive delegation. Inform. Systems Res. 33(2):678–696.Link, Google Scholar
• Henderson RM, Clark KB (1990) Architectural innovation: The reconfiguration of existing product technologies and the failure of established firms. Admin. Sci. Quart. 35(1):9–30.Crossref, Google Scholar
• Hunter DR (2004) Mm algorithms for generalized Bradley-Terry models. Ann. Statist. 32(1):384–406.Crossref, Google Scholar
• Jasperson J, Carter PE, Zmud RW (2005) A comprehensive conceptualization of post-adoptive behaviors associated with IT-enabled work systems. MIS Quart. 29(3):525–557.Crossref, Google Scholar
• Joshi KD, Chi L, Datta A, Han S (2010) Changing the competitive landscape: Continuous innovation through IT-enabled knowledge capabilities. Inform. Systems Res. 21(3):472–495.Link, Google Scholar
• Liang JT, Lin M, Rao N, Myers BA (2025) Prompts are programs too! Understanding how developers build software containing prompts. Proc. ACM Software Engrg. 2(FSE):1591–1614.Google Scholar
• Manning BS, Zhu K, Horton JJ (2024) Automated social science: Language models as scientist and subjects. NBER Working Paper No. 32381, National Bureau of Economic Research, Cambridge, MA.Google Scholar
• Meincke L, Mollick E, Mollick L, Shapiro D (2025) Prompting science report 1: Prompt engineering is complicated and contingent. Preprint, submitted March 4, https://arxiv.org/abs/2503.04818.Google Scholar
• Noy S, Zhang W (2023) Experimental evidence on the productivity effects of generative artificial intelligence. Science 381(6654):187–192.Crossref, Google Scholar
• Oppenlaender J (2023) A taxonomy of prompt modifiers for text-to-image generation. Behav. Inform. Tech. 43(15):1–14.Google Scholar
• Orwig W, Edenbaum ER, Greene JD, Schacter DL (2024) The language of creativity: Evidence from humans and large language models. J. Creative Behav. 58(1):128–136.Crossref, Google Scholar
• Peng S, Kalliamvakou E, Cihon P, Demirer M (2023) The impact of AI on developer productivity: Evidence from GitHub copilot. Preprint, submitted February 13, https://arxiv.org/abs/2302.06590.Google Scholar
• Radford A, Kim JW, Hallacy C, Ramesh A, Goh G, Agarwal S, Sastry G, et al. (2021) Learning transferable visual models from natural language supervision. Meila M, Zhang T, eds. Proc. 38th Internat. Conf. Machine Learn., Proceedings of Machine Learning Research, vol. 139 (PMLR, New York), 8748–8763.Google Scholar
• Rogers EM (2003) Diffusion of Innovations, 5th ed. (The Free Press, New York).Google Scholar
• Romera-Paredes B, Barekatain M, Novikov A, Balog M, Kumar MP, Dupont E, Ruiz FJR, et al. (2024) Mathematical discoveries from program search with large language models. Nature 625(7995):468–475.Crossref, Google Scholar
• Schulhoff S, Ilie M, Balepur N, Kahadze K, Liu A, Si C, Li Y, et al. (2024) The prompt report: A systematic survey of prompting techniques. Preprint, submitted June 6, https://arxiv.org/abs/2406.06608.Google Scholar
• Shahidi P, Rusak G, Manning BS, Fradkin A, Horton JJ (2025) The Coasean singularity: Demand, supply, and market design with AI agents. The Economics of Transformative AI (The University of Chicago Press, Chicago).Google Scholar
• Teece DJ, Pisano G, Shuen A (1997) Dynamic capabilities and strategic management. Strategic Management J. 18(7):509–533.Crossref, Google Scholar
• Vafa K, Bentley S, Kleinberg J, Mullainathan S (2025) What’s producible may not be reachable: Measuring the steerability of generative models. Thirty-ninth Annual Conf. Neural Inform. Processing Systems (San Diego, CA).Google Scholar
• Von Hippel E (2006) Democratizing Innovation (MIT Press, Cambridge, MA).Google Scholar
• Xie Y, Pan Z, Ma J, Jie L, Mei Q (2023) A prompt log analysis of text-to-image generation systems. Ding Y, Tang J, Sequeda J, Aroyo L, Castillo C, Houben G-J, eds. WWW’23: Proc. ACM Web Conf. 2023 (Association for Computing Machinery, New York), 3892–3902Google Scholar
• Yao Z, Jaafar A, Wang B, Yang Z, Yu H (2024) Do clinicians know how to prompt? The need for automatic prompt optimization help in clinical note generation. Proc. 23rd Workshop Biomedical Natural Language Processing (Association for Computational Linguistics, Stroudsburg, PA), 182–201.Google Scholar
• Yu Z (2024) The impacts of AI on scientific labor: Evidence from protein structure prediction. Preprint, submitted February 21, https://doi.org/10.2139/ssrn.4711334.Google Scholar
• Zermelo E (1929) Die berechnung der turnier-ergebnisse als ein maximumproblem der wahrscheinlichkeitsrechnung. Math. Z. 29(1):436–460.Crossref, Google Scholar
• Zhang P, Kamel Boulos MN (2023) Generative ai in medicine and healthcare: Promises, opportunities and challenges. Future Internet 15(9):286.Crossref, Google Scholar
• Zhou E, Lee D (2024) Generative artificial intelligence, human creativity, and art. PNAS Nexu. 3(3):pgae052.Crossref, Google Scholar