Prompt Adaptation as a Dynamic Complement in Generative AI Systems
Under rapid generative AI model iteration, users’ ability to adapt to evolving models critically determines the translation of technological advancement into economic value. Method: This paper introduces *prompt adaptation*—users’ deliberate refinement of input prompts—as a dynamic complementarity mechanism in generative AI. We systematically quantify its impact via online controlled experiments, analysis of over 18,000 real-world human-AI interactions, and image similarity-based evaluation. Contribution/Results: Approximately 49% of DALL·E 3’s performance gain stems from user-initiated prompt adjustments; replacing such human adaptation with automated prompt rewriting incurs a 58% loss in upgrade benefits. These findings demonstrate that user adaptive behavior accounts for nearly half of the value generated by model upgrades, underscoring its pivotal role in human-AI co-creation. The study provides empirical grounding for AI deployment strategies and human-centered interface design.