Tony CantaveWhat I Learned Evaluating AI Image, Video and Audio Models

Part of my week is spent as a freelance AI evaluator. I review output from AI models that generate images, video, audio and user interfaces, and score each one against a structured rubric. It is detailed work, and it has changed how I think about building software.

The first lesson: following instructions matters more than looking good. An image can be beautiful, but if the prompt asked for three people and the model drew four, it failed the task. A plainer result that does exactly what was asked is the better answer. That rule applies to almost everything I build now. Do what the user asked first, then make it pretty.

The second lesson: false positives are worse than misses. When you flag a problem that is not really there, you send the people training the model in the wrong direction. I would rather be precise and miss a small issue than be loud and wrong. In my own projects, that means I test carefully before I call something broken or fixed.

The third lesson: short, clear feedback beats long explanations. A tight verdict that names the exact issue is more useful than a paragraph of opinion. I bring the same habit to code reviews and client conversations.

Working on the evaluation side also makes it clear how much human judgment still sits behind these systems. Models improve because people check their work, carefully and consistently. For anyone moving into AI or software, I recommend trying evaluation work. You learn quickly what good output looks like, and you start to see the gap between a demo and something people can rely on.