Generative AI models are increasingly powering software applications, offering the capability to produce expressive content across varied contexts. However, unlike previous iterations of human-AI design, the emerging design process for generative capabilities primarily hinges on prompt engineering strategies. Given this fundamental shift in approach, our work aims to understand how collaborative software teams set up and apply design guidelines and values, iteratively prototype prompts, and evaluate prompts to achieve desired outcomes. We conducted design studies with 39 industry professionals, including designers, software engineers, and product managers. Our findings reveal a content-centric prototyping approach in which teams begin with the content they want to generate, then identify specific attributes, constraints, and values, and explore methods to give users the ability to influence and interact with those attributes. Based on associated challenges, such as the lack of model interpretability and overfitting the design to examples, we outline considerations for generative AI prototyping.

翻译：生成式AI模型正日益驱动软件应用，能够在不同情境下生成富有表现力的内容。然而，与以往人机交互设计不同，生成式能力的涌现设计过程主要依赖于提示工程策略。鉴于这一根本性转变，本研究旨在理解协作软件团队如何制定和应用设计准则与价值理念，如何迭代构建提示，以及如何评估提示以达成预期目标。我们与39名行业从业者（包括设计师、软件工程师和产品经理）开展了设计研究。研究结果表明，这是一种以内容为中心的原型设计方法：团队从期望生成的内容出发，识别具体属性、约束条件和价值取向，探索让用户能够影响并与这些属性交互的方式。基于模型可解释性不足、设计过度拟合样例等关联挑战，我们提出了生成式AI原型设计的若干考量因素。