How UX personas made our AI training data more inclusive

"My role was straightforward: write queries (prompts and tasks) that would train AI agents to engage meaningfully with users. But as a UXer, one question immediately stood out - who are these users? Without a clear understanding of who the agent is interacting with, it's nearly impossible to create realistic queries that reflect how people engage with an agent. That's when I discovered a glitch in the task flow."

"There were no defined user archetypes guiding the query creation process. Team members were essentially reverse-engineering the work: you think of a task, write a query to help the agent execute it, and cross your fingers that it aligns with the needs of a hypothetical "ideal" user - one who might not even exist. For my first task, I did what any UXer would do: I spoke to real AI users across different domains. One insight stood out: there's a significant difference in how people interact with AI. A UX designer working at a tech company might prompt: "Can you audit the GreenView App Design file in Figma and identify the three frames with the most comments from team members?" A business owner who's not fluent in English might prompt: "I need make list of things finishing in shop." A neurodivergent user struggling to articulate a complex task might type fragmented thoughts, or even need the agent to help structure their promp"

A training role required writing prompts and tasks to teach AI agents to engage meaningfully with users. Lack of defined user archetypes led prompt writers to assume an "ideal" user who often mirrored the team's background. Designing for that ideal user propagates bias because each query teaches the agent what normal interaction looks like. Interviews with diverse AI users revealed wide variation in language, fluency, and communication styles. Examples include technical UX designers using detailed references, non-fluent business owners using fragmented English, and neurodivergent users needing help structuring thoughts. Introducing a UX persona redirected query creation toward realistic, inclusive prompts.

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