Most write-ups about "AI development" quietly conflate two very different activities. One is building software that uses generative AI as a core capability: copilots, retrieval systems, autonomous agents. The other is using generative AI to build software: code generation, test synthesis, legacy modernization. They share a buzzword and almost nothing else. The skills, the risks, and the discipline required are different, and teams that treat them as one thing tend to get burned on both.
If you're shipping a custom AI application, you will run into both at once. This post is a practical map of where each shows up, what tends to break, and how to keep the speed without inheriting the fragility.
Track 1: Generative AI as the Product
When the AI is the feature, the engineering challenge is not "call a model." It's everything wrapped around the call that decides whether the thing is correct, safe, and maintainable.
A few realities that separate a working demo from a deployable application:
