Arpy Dragffy Guerrero
speaker
627 appearances
7 recordings
1 series
first heard May 2026
last heard 6 Aug
Arpy Dragffy Guerrero’s voice in public audio — every appearance, attributed to the second.
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recordings per month · last 12 monthsRecordings per month over the last 12 months — 7 in all, peaking in Jun 2026 with 3.
Appearances
Imagine that Anthropic came to you and said, you know what, it's time to revamp Claude Code.
How would you approach that brief?
I agree with that.
Okay, so wrap us up.
Which AI products are you most excited about right now?
And tell us why and what makes them so special?
And where should people follow along with what you're working on?
Thank you for listening to the Product Impact Podcast.
If you enjoyed this episode, make sure to follow us and leave a comment to inform the conversation.
Welcome to the Product Impact Podcast.
Our guests today are three exceptional researchers from the Microsoft UXR team.
Christopher Monnier, Chuck Kuang, and Wendy Wang lead AI-powered UX research on Microsoft Copilot.
They're responsible for the Consumer App and now expanding into enterprise.
Building a super app, one product designed to handle any use case for any kind of user, comes with challenges that most research teams never face.
So understanding product market fit when your product is everything for everyone.
And proving how to improve when scale makes traditional research methods unmanageable.
Those challenges led this team to pioneer new approaches to participant recruiting and to develop what they call UX evals, a new method for scoring the quality of LLM outcomes that the broader research community has been paying some pretty close attention to.
In this episode, you will learn what usage data hides about whether your AI product is actually working and which metrics matter once adoption stops being enough.
What UX evals are and how they differ from traditional evals and unit tests, and why they close gaps that automated testing just structurally cannot.
We cover which research methods hold up at LLM scale and which ones break down under the weight of real users and how you research a product whose audience is literally everyone.
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