Unlocking the User Experience of Generative AI Applications: Design Patternsand Principles
VINITA TIBDEWAL, SHARODA PAUL, XIAOMAI CHEN, JENNIFER KIM, OBINNA ANYA, MINASHOJAEIZADEH, and ILTERIS KAPLAN, Google LLC, USA
As generative AI technology gains traction in enterprise settings, it is critical to design user experiences that harness its capabilities
and mitigates potential drawbacks. While researchers have established initial design principles for generative AI products [8], we stillneed user validation and the creation of reusable design patterns to further their practical application. In 2023, we conducted extensive
user research to understand users’ mental models and interaction preferences with generative AI applications. This paper presents keyfindings and recommendations to support the development of effective and engaging generative AI end-user experiences in enterprisecontexts. Our investigation combined a UX workshop combined with UX design challenge to generate innovative microinteraction [4]patterns, followed by prototyping and evaluating three generative AI application scenarios which yielded qualitative insights into
user perceptions and behaviors. We present core design guidelines derived from this research, along with associated design patterns,offering practical guidance for development of generative AI applications.
CCS Concepts: • Human-centered computing → Empirical studies in HCI ; User studies.
Additional Key Words and Phrases: Generative Artificial Intelligence, Design patterns, Microinteractions
ACM Reference Format:
Vinita Tibdewal, Sharoda Paul, Xiaomai Chen, Jennifer Kim, Obinna Anya, Mina Shojaeizadeh, and Ilteris Kaplan. 2018. Unlocking theUser Experience of Generative AI Applications: Design Patterns and Principles. In Honolulu ’24: ACM CHI Conference on Human Factors
in Computing Systems,May 11–16, 2024, Honolulu, Hawai’i. ACM, New York, NY, USA, 5 pages. https://doi.org/XXXXXXX.XXXXXXX

1 INTRODUCTION
The generative AI revolution is in full swing, compelling enterprises to rapidly develop and deploy generative AI
applications to maintain a competitive edge. To ensure this technology delivers optimal user experiences, a thoroughunderstanding of user perceptions, mental models, and user interactions with generative AI applications is critical.
Previously, Weisz et. al. [8] identified seven design principles for generative AI applications through literature review,
but the principles were not validated with users. Liu and Chilton [5] evaluated design concepts and provided design
recommendations for end-user interactions, however, their work focused on effective prompt engineering for text-toimage models. Others focused on suggestions and ideas for one-shot prompt engineering for text generation. [2, 3, 7].
In this paper, we describe a comprehensive effort conducted within our organization to create a collection of usertested microinteraction patterns for engaging with generative AI applications, and research-backed guidance for using
those design patterns in generative AI applications.
Our UX team works closely with our company’s Cloud enterprise customers to help them design and deploy
generative AI applications for their customers (or "end-users"). These enterprise customers are often looking for
guidance on how to design the end-user experience of the generative AI applications they are developing. In order toPermission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are notmade or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for componentsof this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post onservers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from permissions@acm.org.
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Manuscript submitted to ACM
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CHI Conference ’24, May 11-16, 2024, Honolulu, Hawai’iTibdewal, et al.

provide this guidance, we decided to explore the design space of end-user interactions with generative AI applications
through a three-step approach. First, we launched a łdesign challengež within our UX organization to solicit a varietyof microinteraction designs, guided by generative AI design principles from literature [8]. Microinteraction designs
were submitted by a variety of UX roles, like UX designers, UX researchers, content strategists, and UX engineers.
Next, we created three different prototypes of fictitious chat-based generative AI applications that incorporated the top
designs submitted. And finally, we collected qualitative feedback from 5 users on each of these prototypes (with a totalof 15 participants) through unmoderated and moderated user research sessions. In this paper we will focus on the user
research methods and findings.
2 METHODS
The research goals of this project were to Ð a) Explore and evaluate existing design guidelines for generative AI products
and applications; b) Understand how users interact with generative AI applications; c) Evaluate design principles andmicrointeractions needed to support the user experience. To develop effective microinteractions for generative AIapplications, we began with a UX workshop. Designers and researchers collaborated and identified four key phases inthe user journey of any generative AI applications:• Before state / Zero state: The initial experience prior to user input• During question / Entering prompt: The core interaction of providing input and receiving AI-generated output• After question / Follow-up state: User actions refining or expanding the AI response• Resolution state: The point where the user’s goal is achieved or the interaction ends
Next, we launched a design challenge within the same group, leveraging generative AI design principles [8] to guidethe generation of microinteractions within the above-mentioned four states. Using the microinteractions, we developed
three different generative AI application prototypes. The prototypes were of a social messaging app, a generative AI
chatbot for a travel website, and a generative AI search app for a travel website.
The first prototype was evaluated through unmoderated user sessions on UserTesting [6] platform with 5 participants,
providing qualitative audio and video feedback. We soon realized that unmoderated user research was not ideal as
it didn’t allow us to probe participants with follow-up questions; asking follow-up questions would provide deeper
insights on the users’ interactions with the prototypes. So, we evaluated the next two prototypes using moderated
structured interviews on UserTesting to collect detailed qualitative feedback.
The participants self-identified as having used various consumer generative AI tools (e.g. ChatGPT, Bard, DaLL-E)
and had recent experience in tasks related to the ones we were evaluating (e.g. trip planning).
3 OUTCOME
We identified four key insights from this research: 1) Help users explore generative variability; 2) Help users buildtrust; 3) Give users control over generated responses; 4) Improve results through user feedback. These insights validate
the design guidelines from Weisz et, al. [8]. We additionally highlight top microinteractions that can be used whiledeveloping user-friendly generative AI applications.
3.1 Help users explore generative variability
Generative variability is a key feature of generative AI applications. Understanding that generative AI produces variedoutputs unlocks its full potential for exploration and creativity.
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Unlocking the User Experience of Generative AI Applications: Design Patterns and Principles CHI Conference ’24, May 11-16, 2024, Honolulu, Hawai’i

Our studies revealed that users valued microinteractions that enabled them to harness this variability. A simple"Generate new prompts" button (Fig. 1) for regenerating results proved popular. As well as, users appreciated the abilityto specify parameters for refining further output. For example, users appreciated a "Generate Again" button as a way toadjust a generated trip itinerary based on price or popularity of the attraction (Fig. 3b). These microinteractions should

Fig. 1. Landing page of generative AI travel chatbot that provides "prompt suggestions" and "generate prompts" option
Fig. 2. Results page of generative AI travel chatbot with citation for the recommendation
Fig. 3. Generative AI travel search application; (3a) Drop-down menu for editing the prompt; (3b) łGenerate again" button with adrop-down menu

be leveraged and highlighted to help users understand generative AI’s capacity for varied and unexpected results.
While users valued the option to explore variable outputs, they expressed uncertainty about how to end the cycle. For
example, when the users were seeing repetitive or irrelevant results, they wanted the app to indicate so. Our suggestion
is to provide color-coding or subtle visual designs to indicate when results are becoming less distinct. Additionally, theapp can provide notifications when newer suggestions or results are available.
3.2 Help users build trust
Users felt more confident in the generative AI responses when provided with the means to verify the responses. Citations
or grounding [1] empowered users to evaluate if the generated response is correct and up-to-date. Examples include
access to primary sources used for creating summaries (Fig. 2), or links to hotel websites for checking descriptions andpricing (Fig. 4).
3.3 Give users control over generated responses
User control was another important factor that users cared about while interacting with the prototype. Having control
over the generated responses made them feel more confident about using generative AI apps. This can be done bypresenting multiple generated responses for the user to select from.
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CHI Conference ’24, May 11-16, 2024, Honolulu, Hawai’iTibdewal, et al.

Additionally, guiding users in effective prompt design is crucial. Many users may not grasp the relationship between
prompt quality and output. Microinteractions like "suggested prompts" chips (Fig. 1) and drop-downs (Fig. 3a) for
editing prompt parameters provide accessible ways to improve users’ prompting skills.

Fig. 4. Generative AI travel chatbot application which allows users to provide feedback on the generated content

3.4 Improve results through user feedback
To refine generated information and ensure its quality aligns with user needs, prioritize strategic collection of user
feedback. In our studies, we found that successful feedback integration depends heavily on placement and timing of
feedback collection UI elements. Users sometimes misinterpreted feedback requests as an indication to change topics,
hindering their user flow.
We recommend crafting an "in-the-moment" approach by directly associating feedback requests with the specificresponses for which feedback is sought (Fig. 4). Furthermore, soliciting reasons behind user ratings will uncoveractionable insights for improving the quality of generated content.
4 CONCLUSION
Through extensive iterative design and research, we identified possible microinteractions as well as validated existing
design guidelines, in the context of generative AI applications. We learned valuable lessons for evaluating interfaces
with generative outputs. Moderated user studies yielded greater insights than unmoderated sessions, demonstrating theimportance of facilitation, especially for a relatively new domain like generative AI. With respect to designing generative
AI interactions, we would like to highlight that users highly valued transparency about the system’s workings. Moreover,
empowering users through control mechanisms and focused feedback opportunities directly fosters a greater sense oftrust in generative AI applications.
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ACKNOWLEDGMENTS
We’d like to express our sincere gratitude to several key individuals and our team who were instrumental in thedevelopment and launch of this UX program: Shantanu Pai, whose vision and direction were the driving force behind
this project; Jennifer Otitigbe, who provided invaluable research expertise and guidance; Liz Merritt, whose efforts
ensured seamless collaboration and effective communication of our results both internally and externally; and thewhole Cloud AI team, for their enthusiastic contributions to the brainstorming workshop and design challenges.
REFERENCES[1] Simon Attard. 2023. Grounding Generative AI. https://medium.com/@simon_attard/grounding-large-language-models-generative-ai-526bc4404c28[2] Paul Denny, Viraj Kumar, and Nasser Giacaman. 2022. Conversing with Copilot: Exploring Prompt Engineering for Solving CS1 Problems Using NaturalLanguage. Proceedings of the 54th ACM Technical Symposium on Computer Science Education V. 1 (2022). https://api.semanticscholar.org/CorpusID:253157479[3] Cobus Greyling. 2022. Prompt Engineering, Text Generation Large Language Models. https://cobusgreyling.medium.com/prompt-engineering-textgeneration-large-language-models-3d90c527c6d5[4] Alita Joyce. 2018. Microinteractions in User Experience. https://www.nngroup.com/articles/microinteractions/[5] Vivian Liu and Lydia B. Chilton. 2021. Design Guidelines for Prompt Engineering Text-to-Image Generative Models. Proceedings of the 2022 CHI
Conference on Human Factors in Computing Systems (2021). https://api.semanticscholar.org/CorpusID:237513697[6] UserTesting platform. 2024. UserTesting website. https://www.usertesting.com/[7] Laria Reynolds and Kyle McDonell. 2021. Prompt Programming for Large Language Models: Beyond the Few-Shot Paradigm. Extended Abstracts ofthe 2021 CHI Conference on Human Factors in Computing Systems (2021). https://api.semanticscholar.org/CorpusID:231925131[8] Justin D. Weisz, Michael J. Muller, Jessica He, and Stephanie Houde. 2023. Toward General Design Principles for Generative AI Applications 130-144.
In IUI Workshops. https://api.semanticscholar.org/CorpusID:255825625

Received 26 February 2024; accepted 15 March 20245