Why measuring AI feature success rates is essential for knowing whether they actually work.
Adapted from @faridmovsumovWhen building AI features, always build a tracking system that lets you measure the success rate. Building AI features is cool. But tracking the success rate matters even more because it is the only way to know whether they are actually working. Otherwise, you can test a few happy paths and think you built a successful feature. I recently made a lot of optimizations to our magic fill feature, which fills option values based on product title and color by analyzing product images. The success rate for option values is around 94%, and for color assignment it is around 87% now When I first built it, the first version had a success rate of around 60% Additionally, if the user doesn't accept our suggestion and enters a different value instead, we save it and later use it to improve the AI. This data is real gold for AI.
