Generative AI models are increasingly being integrated into human task workflows, enabling the production of expressive content across a wide range of contexts. Unlike traditional human-AI design methods, the new approach to designing generative capabilities focuses heavily on prompt engineering strategies. This shift requires a deeper understanding of how collaborative software teams establish and apply design guidelines, iteratively prototype prompts, and evaluate them to achieve specific outcomes. To explore these dynamics, we conducted design studies with 39 industry professionals, including UX designers, AI engineers, and product managers. Our findings highlight emerging practices and role shifts in AI system prototyping among multistakeholder teams. We observe various prompting and prototyping strategies, highlighting the pivotal role of to-be-generated content characteristics in enabling rapid, iterative prototyping with generative AI. By identifying associated challenges, such as the limited model interpretability and overfitting the design to specific example content, we outline considerations for generative AI prototyping.

翻译：生成式AI模型正日益融入人类任务工作流，使其能够在广泛情境中生成富有表现力的内容。与传统的人机协同设计方法不同，生成式能力设计的新方法高度聚焦于提示工程策略。这种转变要求我们更深入地理解协作软件团队如何建立并应用设计准则、迭代式地构建提示原型，并通过评估实现特定目标。为探究这些动态机制，我们与39位行业专业人士（包括UX设计师、AI工程师和产品经理）开展了设计研究。研究发现揭示了多利益相关者团队在AI系统原型设计中涌现的新实践与角色转变。我们观察到多样化的提示构建与原型设计策略，突显了待生成内容特征在实现生成式AI快速迭代原型设计中的关键作用。通过识别相关挑战（例如模型可解释性有限以及设计过度拟合特定示例内容等问题），我们提出了生成式AI原型设计的重要考量维度。