How I Approach Evaluation When Building AI Features

Building an AI feature is not the same as shipping traditional software. In classic software, you write code, test it, and deploy it. Deployment is usually a finish line. With AI features, deployment is just the beginning. That is one of the biggest mindset shifts I have had while working on AI systems. The question is not only whether a feature works during development. The bigger question is whether it keeps working well when real users, messy inputs, changing data, and production constraints enter the picture. That is why I take evaluation seriously. Not as a one-time quality check. Not as something to do right before launch. But as an ongoing part of building the product. Why evaluation has to be continuous AI systems are different because their behavior is not fully fixed. Even if the code around the model does not change, the outputs can still shift because of: new user inputs different context data retrieval quality changes prompt changes model updates distribution drift in real