AI tools move fast—but without thoughtful evaluation, they can also create confusion, frustration, and experiences users don’t trust.

This guide shows UX teams how to apply their existing research and design skills to AI evaluation. You’ll learn what to evaluate, what to do with your findings, and how to set up a process that lasts.

What you’ll find inside:

How to build practical AI eval workflows

Move from informal “vibe checks” to structured evaluations that help your team judge AI quality with confidence.

Strategies to define what “good” looks like

See how UXRs, designers, and PMs can create evaluation criteria rooted in real user experience—not just model performance.

A real evaluation example

See how the Dscout team evaluated our Refine Your Questions feature and what we took away.

See why this is a big moment for UX teams

Find out why AI evaluation is a role UX teams are ready to step into.

“Research is essential to maintaining a vision of what our products are actually meant to do, what value they're meant to deliver, and exploring how AI can fulfill those promises, instead of the promise of simply “AI.””

- Nathan Reiff Product Manager at Dscout