Intro
As enterprises race towards building and deploying generative AI applications, there are many open questions about how to design the end user experience of these applications to take advantage of this powerful technology, while avoiding its pitfalls. The Cloud AI and Industry Solutions team has been conducting extensive design and user research activities to understand how users perceive and engage with generative AI applications. Here we share our learnings in order to help enterprise customers build useful and delightful end user experiences for their generative AI applications.
Though generative AI applications have exploded in popularity in the last few months, very little is understood today about users’ mental models, perceptions, preferences, and challenges while interacting with these applications. Our user experience (UX) team launched an organization-wide effort aimed at creating a collection of user-tested micro-interaction design patterns and research-backed guidance for using those design patterns in generative AI applications.
For this, we first launched a “design challenge” within our UX team to solicit a variety of micro-interaction designs, guided by generative AI design principles. Next, we created prototypes of various chatbot applications that incorporated those designs. Finally, we collected qualitative feedback from 15 external users on these prototypes through various user research methods, like moderated and unmoderated user feedback sessions.
Below, we distill our takeaways in the form of design principles and design patterns to illustrate those principles.
Key takeaways
1. Help users explore generative variability
One of our key learnings was that we should help users explore generative variability, that is the ability of a generative AI application to produce a range of outputs for the same prompt or question. Additionally, we need to help users understand triggers and end points of an interaction with a generative AI model.
- Generative variability: Generative variability is a salient feature of generative AI apps. In our studies, we found that users liked micro-interactions that helped them take advantage of this “generative variability” through the “Generate Again” button (Fig 1) that allowed them to regenerate results for their prompt . They also liked the ability to specify dimensions or preferences for re-generating results, e.g., providing options to re-generate a trip itinerary based on the price or popularity of the attractions (as in the drop-down in Fig 1).
