Design Principles for Generative AI Applications

We present six principles for the design of generative AI applications that address unique characteristics of generative AI UX and offer new interpretations and extensions of known issues in the design of AI applications. Each principle is coupled with a set of design strategies for implementing that principle via UX capabilities or through the design process.

Generative AI technologies have introduced a new paradigm of human-computer interaction, what Nielsen refers to as “intent-based outcome specification” [127]. In this paradigm, users specify what they want, often using natural language9, but not how it should be produced. One challenge of this paradigm stems from the distinguishing characteristic of generative AI: it generates artifacts as outputs and those outputs may vary in character or quality, even when a user’s input does not change. This characteristic has been described by Weisz et al. [182] as generative variability, and it provides what Alvarado and Waern [6] describe as an “algorithmic experience,” raising questions on appropriate types of user control, levels of algorithmic transparency, and user awareness of how the algorithms work and how to effectively interact with them.