A Practical Guide to AI Code Generation

The Seductive Promise: Build in Days, Not Months There is something undeniably compelling about modern AI code generation. You describe a feature. It scaffolds the controllers. It writes the repository layer. It generates DTOs, tests, migrations, and configuration. What used to take weeks can now appear in minutes. For greenfield projects especially, the acceleration feels almost unfair. A single developer can prototype at a pace that previously required a small team. Refactoring feels assisted rather than manual. Exploration becomes interactive. If you measure success by initial velocity , AI looks like a revolution. And in many contexts, it is. But velocity is only one axis of software quality. The real question is not: “Can AI generate my application?” It clearly can. The real question is: “What happens after generation?” That is where engineering discipline becomes decisive. 1. The Licensing Risk This topic tends to be exaggerated and underestimated at the same time. What is the co