  Text-to-image models are enabling efficient design space exploration, rapidly
generating images from text prompts. However, many generative AI tools are
imperfect for product design applications as they are not built for the goals
and requirements of product design. The unclear link between text input and
image output further complicates their application. This work empirically
investigates design space exploration strategies that can successfully yield
product images that are feasible, novel, and aesthetic, which are three common
goals in product design. Specifically, user actions within the global and local
editing modes, including their time spent, prompt length, mono vs.
multi-criteria prompts, and goal orientation of prompts, are analyzed. Key
findings reveal the pivotal role of mono vs. multi-criteria and goal
orientation of prompts in achieving specific design goals over time and prompt
length. The study recommends prioritizing the use of multi-criteria prompts for
feasibility and novelty during global editing, while favoring mono-criteria
prompts for aesthetics during local editing. Overall, this paper underscores
the nuanced relationship between the AI-driven text-to-image models and their
effectiveness in product design, urging designers to carefully structure
prompts during different editing modes to better meet the unique demands of
product design.
