What ChatGPT does well (and doesn’t do well) for User Experience
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This is the first installment of how to use ChatGPT for User Experience, diving into each of the artifacts listed and is not comprehensive of all processes. Enjoy the ride.
I’ve worked in environments where artificial intelligence was part of the equation for eight years. It was everything from audience segmentation to field extraction, but it was a black box that required building precise models: You fed data in and out came an answer that required some level of human review or nuance.
But then, November 30, 2022, was the day everything changed. That’s when ChatGPT entered the scene.
We were blown away because it was clear that not only could it be added to our product as a feature where people could ask questions about contracts, but it would also change how we work.
It will shake up the way users experience things so much that we’ll have to rethink many of our processes in the future. A complete transformation of the field, so to speak.
Michio Kaku said ChatGPT is just a fancy tape recorder. True.
Is it a wonderful starting point? Also true.
We need to understand and use generative AI tools like ChatGPT. If we don’t, someone else will. Here are some tips on what works and what doesn’t when using it.
What ChatGPT does well
Discovery and Draft Specifications
Need to create personas in a pinch? ChatGPT’s got you covered.
Need multiple problem statements using just a single prompt? ChatGPT is there.
Need user stories for a basic feature? ChatGPT can do that, too.
Need to draft every written artifact you can think of for a feature? Yep, ChatGPT handles it all to give you something that’s good enough to get you started and greatly accelerate the process.
I tried it out on an example feature, and because of how much it sped up how we worked, we use it daily at work. From creating fake data, writing user stories, or looking for analogous inspiration, it’s the baseline of any feature. ChatGPT speeds up early discovery by giving you quick access to a ton of information that is good enough to get started, so you aren’t staring at a bunch of web pages or interviewing many users to get started, helping designers gather and make sense of data quickly.
Some professionals I know also enrich personas with data so that you can converse with an “artificial” user. It’s not a replacement for the real thing but a good way to ask better questions.
Whether you’re identifying relevant studies, breaking down complex ideas, or developing new hypotheses, ChatGPT is a wonderful tool to start with.
It also boosts brainstorming sessions with fresh perspectives and helps researchers explore new ideas efficiently. Plus, it makes the literature review process a breeze, saving you time and letting you focus on experimental design and analysis.
Example prompt: Create multiple problem statements in a “How might we?” format for searching through a repository of documents.
Artifacts: Personas, Analogous Inspiration, Competitive Analysis, User Research Questions, Problem Statements, Predicted Outcomes, User Stories, and Usability Testing Questions.
Realistic Data
One of the most time-consuming activities I have had in user experience is creating fake data. I spent hours and hours building out this in wireframes for usability testing prototypes—something that is both fun and time-consuming.
That process is gone. ChatGPT saves a ton of time doing that now.
Creating fake data with ChatGPT is a breeze because the model quickly generates contextually relevant and diverse information, especially if you know what fields you want to use. By leveraging its vast training data, ChatGPT can simulate realistic user inputs, behaviors, and scenarios that mimic real-world data patterns.
And it’s not just about some data — it can create thousands of lines of realistic data to use in development environments.
This capability allows UX designers to prototype and test interfaces, speeding up the design iteration process. It’s like having an instant, versatile sandbox to explore and refine user experiences before launching into the real world.
Example prompt: Create a table of realistic data for 25 users with the following fields: First Name, Last Name, Email Address, Role (Administrator, Edit User, or Read-Only User), Active Status (yes or No), Added Date, and Last Updated Date in YYYY-MM-DD format.
Artifacts: Wireframe content
User Assistance Copy
As a first draft before you even have the first wireframes, ChatGPT does a good job at writing draft user assistance copy for features. This is due to several key advantages.
Its adaptability enables ChatGPT to generate content tailored to meet the users where they are at. Whether explaining basic functionalities or troubleshooting intricate issues, ChatGPT can adjust its language and depth of detail accordingly.
Because it’s a fancy tape recorder that references previous well-known contexts, it can generate a good first draft from which to start.
ChatGPT’s consistency ensures that user assistance copy maintains a uniform quality and tone across interactions. You can even specify the style and tone, such as business casual or formal, so it’s tailored to the audience. This builds trust and enhances the overall user experience as long as it is edited and reviewed accordingly.
Example User Assistance Prompt: Write user assistance content for a document search table where you can search by keyword or filter by certain fields. The actions are searching by keyword (boolean or non-boolean), selecting a filter, adding a filter, clearing a filter, and sorting by field.
Artifacts: Wireframes, in-application content, knowledge base articles.
What ChatGPT does not do well
Wireframing
Until someone can write perfect user stories or product requirements documents, I’m convinced that ChatGPT may assist designers but will never replace them.
Wireframing isn’t just about creating a visual blueprint; it’s about understanding user needs, iterating on ideas, and fostering stakeholder collaboration. There’s a lot of nuance in
While AI can help generate wireframes faster, it lacks the human intuition, empathy, and domain-specific knowledge required to understand and solve user problems deeply. For example, you can design a search experience, but every use case is slightly different depending on the domain. Searching for merchandising and searching for documents, for example, is a very different experience.
AI-generated wireframes may overlook the nuanced considerations and contextual insights UX designers bring through research and experience.
Therefore, while AI can assist, it won’t replace the critical, human-centered wireframing process in UX design.
Artifacts: Wireframes
Nuance
Respect Regulatory Guidelines
When I had my first conversations with designers about Open AI, we discussed several situations where ChatGPT was a bad fit—music authoring, writing, and other copyrighted material were examples—but the field I work in, Legal Tech, is actually one of the best fits for what ChatGPT can provide.
ChatGPT as a first draft without nuance? Sure.
As the final product without a “human in the loop”? Not a chance. There should always be a “human in the loop” to double-check the work, specifically in nuanced situations involving copy and specfications.
Regulatory environments often require precise and unambiguous communication backed by legal and compliance standards (instructions around banking and finance come to mind). ChatGPT, proficient in generating human-like text, cannot accurately comprehend complex legal jargon or nuanced regulatory frameworks.
This can lead to inaccuracies, misunderstandings, or even hallucinations in critical communication, potentially resulting in legal liabilities or non-compliance issues for end users.
ChatGPT excels in many conversational and informational tasks. Still, it is limited: Fancy tape recorders don’t understand complex regulations and potential biases that make them unsuitable for regulatory situations where precision and compliance are paramount.
There will always be a human in the loop here.
What’s Next
How we do early ideation and discovery is already changing, especially at the consumer end of user experience. This is reflected in how user experience teams transform as a forcing function to use these tools.
However, we are very far from completely replacing designers. There may be the need for even more designers going forward because many applications will be transformed for this new era — making knowledge of these tools more important.
We’ve adapted before, and we can do it again. Act accordingly.
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