
A/B testing is great for comparing two versions of a design, but multivariate testing helps teams evaluate multiple design element combinations at once. Here’s how both methods work, how they differ, and when UX teams should use each one.

Design engineering has always lived between design and code. But with AI tools turning prompts into interfaces and code into editable canvases, that bridge is becoming a new way of building.

A three-week mobile banking project taught me that the “proper” UX process is not always realistic. Sometimes, the better approach is to work with what you know, identify what you still need to learn, and make the strongest decision possible under real constraints.

A/B testing compares two versions of a design to see which performs better with real users. Here’s how UX teams can use it to test hypotheses, measure outcomes, and make smarter product decisions.