uxGPT: Creating Fake Data with prompts and the uxGPT Fake Data helper

Part of a series using Chat GPT to accelerate the user experience process.

Fake data—the bane of existence for user experience designers. Creating this data is time-consuming and requires much imagination and testing of different content.

I have spent countless hours repeatedly refining this content to ensure the wireframes represent what the application would display. Besides using Bob Ross Ipsum or the NSFW Samuel L. Jackson Ipsum, I always prefer to simulate the real experience as closely as possible.

Representative data is ridiculously important for several reasons.

It serves as a placeholder that mimics real-world content, helping visualize how the interface will function and behave with actual data. As it simulates how users interact with the product, we assess usability and functionality more realistically.

Fake data identifies potential design flaws early on. By integrating representative content, we can better understand how different data types and volumes affect layout. This enables us to test edge cases and ensure the interface remains intuitive and efficient across various scenarios.

Presenting wireframes with fake data facilitates more meaningful discussions and decisions during design reviews. It shifts the focus from hypothetical situations to concrete examples, prompting productive dialogues about content priorities, information hierarchy, and interaction patterns.

You can’t test your design without having the right representative data in place.

Now, Chat GPT accelerates this. It saves hours of time and effort and creates more realistic content to test your concepts.

You can start with a prompt and refine it quickly. Chat GPT will create realistic-looking content and ensure the data looks correct, down to addresses and phone numbers that match the right metropolitan areas.

I’m going to show you how to do it.

Start With A Prompt

I would start with the following sentence: “Generate a table of realistic fake data with 25 rows for a (topic) application.” It’s a great way to start because Chat GPT will give you a table of information that it believes is appropriate for the topic you select and suggest fields.

The fields don’t have to be right; they give you a baseline. I always state “realistic fake data” as part of the prompt because it generates more representative data.

Example Topics

• Tasks
• User Profiles
• Products
• Events
• Projects
• Settings

Prompt

Generate a table of realistic fake data with 25 rows for a customer relationship management application.

Refine Columns

Once you get the result you want from the initial table, you can review it with your stakeholders if you have the right columns. If you don’t, adding them to the prompt is easy. I’ll list the columns and add them below. The revisions are in bold.

Prompt

Generate a table of realistic fake data with 25 rows for a customer relationship management application with the following columns: Customer ID, Name, Email Address, Phone, Company, Address, Industry, Revenue, First Contacted, Last Contacted, and Next Follow-Up.

Refine Format

Let’s refine the format.

Fake data in the proper format is crucial because it accurately reflects user behaviors and needs, aligning closely with real-world scenarios. It also gives the engineers the proper context for formatting the data and solves many of the what-it-looks-like questions.

It also handles localization very well, with a few examples listed below.

Format Questions

• What are address formats from around the world?
• What are currency formats from around the world?
• What are phone number formats from around the world?
• What are time formats from around the world?
• What are company formats from around the world?

Formats

• US Street Address in standard format
• UK Street Address in standard format
• US Phone Number in standard format
• Date in YYYY-MM-DD format
• Revenue in 7-digit US format
• Revenue in 7-digit German format
• Revenue in 7-digit Russian format

Prompt

Generate a table of realistic fake data with 25 rows for a customer relationship management application with the following columns: Customer ID that’s not sequential, Name, Email Address, Phone in US format or EU format, Company, Industry, Revenue in 7-digit US or German or Russian format with proper localization, First Contacted in YYYY-MM-DD format, Last Contacted in YYYY-MM-DD format, Next Follow-Up in YYYY-MM-DD format.

Try out this Custom GPT?—?uxGPT Fake Data

Don’t want to do the work yourself? Not a problem. I’ve done a lot of the legwork for you.

Try this custom GPT at uxGPT Fake Data.

I would love to get feedback.