Generative AI (GenAI) models excel in their ability to recognize patterns in existing data and generate new and unexpected content. Recent advances have motivated applications of GenAI tools (e.g., Stable Diffusion, ChatGPT) to professional practice across industries, including product design. While these generative capabilities may seem enticing on the surface, certain barriers limit their practical application for real-world use in industry settings. In this position paper, we articulate and situate these barriers within two phases of the product design process, namely"getting the right design"and"getting the design right,"and propose a research agenda to stimulate discussions around opportunities for realizing the full potential of GenAI tools in product design.
- Generative AI, including ChatGPT and art generators like DALL-E 2, Stable Diffusion, and Midjourney, is gaining popularity. - People are trying to figure out how to make the AI outputs match their vision. - Prompt engineering is crucial for unlocking the capabilities of generative AI. - Elaborate prompts are not necessarily better for image generation. - Style keywords can be used to create a variety of interesting images. - Prompt engineering is mostly about trial and error. - Including an image as part of the prompt can influence the generated output.
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