As generative AI systems rapidly improve, a key question emerges: how do users adapt to these changes, and when does such adaptation matter for realizing performance gains? Drawing on theories of dynamic capabilities and IT complements, we study prompt adaptation--how users adjust their inputs in response to evolving model behavior--using a common experimental design applied to two preregistered tasks with 3,750 total participants who submitted nearly 37,000 prompts. We show that the importance of prompt adaptation depends critically on task structure. In a task with fixed evaluation criteria and an unambiguous goal, user prompt adaptation accounts for roughly half of the performance gains from a model upgrade. In contrast, in an open-ended creative task where the space of acceptable outputs is effectively unbounded and quality is subjective, performance improvements are driven primarily by model capability; prompt adaptation plays a limited role. We further show that automated prompt rewriting cannot generally substitute for human adaptation: when aligned with task objectives, it can modestly improve performance, but when misaligned, it can actively undermine the gains from model improvements. Together, these findings position prompt adaptation as a dynamic complement whose importance depends on task structure and system design, and suggest that without it, a substantial share of the economic value created by advances in generative models may go unrealized.

翻译：随着生成式AI系统的快速改进，一个关键问题浮现：用户如何适应这些变化，以及这种适应何时对实现性能提升至关重要？借鉴动态能力理论和IT互补理论，我们通过一个通用实验设计研究了提示适应——即用户如何根据模型行为的演变调整其输入——该设计应用于两项预注册任务，涉及总计3,750名参与者提交了近37,000条提示。研究表明，提示适应的重要性关键取决于任务结构。在具有固定评估标准和明确目标的任务中，用户提示适应约占模型升级所带来的性能增益的一半。相比之下，在开放式创意任务中，可接受输出的空间实际上是无界的且质量是主观的，性能改进主要由模型能力驱动；提示适应的作用有限。我们进一步证明，自动提示改写通常无法替代人类适应：当与任务目标一致时，它可以适度提升性能，但当不一致时，它可能会积极削弱模型改进带来的增益。综合来看，这些发现将提示适应定位为一种动态补充，其重要性取决于任务结构和系统设计，并表明若缺乏这种适应，生成模型进步所创造的经济价值可能有相当一部分无法实现。