As generative AI technologies are pressed into service in workplace settings, current approaches to account for the contexts in which such technologies are used fall short of users' expectations and needs. This paper empirically demonstrates, through expert interviews, both how these tools fail to account for users' context and how users deploy concrete strategies address such failures. The paper analyzes how context is variously conceptualized by tool developers, users, and social scientists to identify specific pitfalls inherent in computational approaches to context. Multiple distinct contexts tend to collapse into one another or rot, degrading over time, reducing the utility of any efforts to account for context. The paper concludes with a provocation to shift from an indiscriminate collection of context-relevant data toward a more interactional set of practices to embed GenAI systems more appropriately into users' contexts of use.

翻译：随着生成式AI技术被应用于工作场景,当前处理此类技术使用情境的方法未能满足用户的期望和需求。本文通过专家访谈实证展示了这些工具如何未能考虑用户的具体情境,以及用户如何采用具体策略应对此类失败。本文分析了工具开发者、用户和社会科学家如何以不同方式概念化"情境",识别了计算式情境处理方法中固有的具体陷阱。多种不同的情境往往相互重叠或退化,随时间推移降低实用性,削弱任何处理情境的努力成效。本文最后提出一个挑战性观点:应从无差别收集情境相关数据转向更具互动性的实践方式,使生成式AI系统更恰当地嵌入用户的使用情境。