A/B Testing

What is A/B testing?

Want to know if a change actually helps? The simplest way is to try two versions at once. A/B testing splits users randomly into two groups: group A sees the old version, group B sees the new one. Then you compare how each performs — which one people like more, which converts better.

Why it matters especially for AI

You can't tune prompts by feel
Reword something and you can't just say "I think it's better". You need data on whether satisfaction actually rose or fell.
Model updates are risky
A new model may be smarter on some tasks and worse on others. A/B testing caps that risk: try it on a small slice first, roll out fully once it's stable.

How to run a decent A/B test

Change one variable at a time
Compare a single change, or you won't know which one made the difference.
Random assignment
Split users randomly, so a biased sample doesn't skew the result.
Run long enough, watch the right metrics
Don't judge by a day or two, and don't stare at one number. Satisfaction, retention and cost all matter.

What problem it solves

Without A/B testing, a lot of "optimization" is just guessing — and often turns out to be a step backward. With it, every change comes with data behind it: better, worse, or no difference, you can see it plainly.

Bottom line: A/B testing lets the data vote for you — run both versions at once and keep whichever one the numbers favor.