Prompting for products: Investigating design space exploration strategies for text-to-image generative models

TL;DR

This paper empirically explores how different prompt strategies in text-to-image models affect the quality of product design images, emphasizing prompt structure and editing modes to meet design goals.

Contribution

It provides insights into prompt structuring and editing modes that improve the effectiveness of text-to-image models for product design applications.

Findings

Multi-criteria prompts enhance feasibility and novelty during global editing.

Mono-criteria prompts improve aesthetics during local editing.

Prompt structure and editing mode significantly influence design outcomes.

Abstract

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.