AI coding tools have changed how fast an app can be built. They have not removed the need for judgement. After using them across many projects, these are the patterns that hold up.
Where they help
- Starting quickly. Turning a clear description of a screen into working first-draft code.
- Repetitive work. Forms, lists, settings screens and similar parts that follow a pattern.
- Explaining errors. Pasting an error message and asking what it means often saves a lot of searching.
- Tests and clean-up. Drafting tests and tidying code you already have.
Where they struggle
- Vague instructions. “Make it better” produces random changes. Specific, testable instructions work far better.
- Large requests in one go. Asking for a whole app at once gives code that is hard to review and harder to fix.
- Sensitive areas. Payments, sign-in, privacy and data storage need careful human review.
Habits that make it work
- Work in small steps. Build one screen or feature, run it, then move on.
- Test on a real device. Something that works in a simulator can still feel wrong in your hand.
- Use version control. Save a checkpoint before every big change so you can go back.
- Read what changed. Even if you are not a developer, look at what the tool modified and ask it to explain anything you do not understand.
- Keep secrets out of prompts. Never paste API keys, passwords or private user data into a tool.
You are still the product owner
The tool writes code. You decide what to build, what good looks like, and when it is ready for real users. People who learn to direct and check AI tools get better results than people who accept whatever comes back.
This is the way of working we teach in the App Development course, and the way our own product team works.