Test AI-generated software

Combine automated checks, human QA and real-device testing instead of trusting a generated answer.

What this step is for.

Generated code can compile and still be wrong. Testing should include fast automated checks, scenario tests and human inspection. For mobile software, a physical device can expose lifecycle, permissions, layout and interaction problems that do not appear in a desktop preview.

Keep the scope small enough that you can inspect the result yourself. AI can accelerate the work, but it should not erase the distinction between a suggestion, a changed file, a successful build and a verified product.

Take one concrete action.

Choose three acceptance scenarios for your project. Run them after a clean start. Record expected versus actual behavior and fix one deliberately introduced defect.

Turn the idea into evidence.

Do the action in a disposable or backed-up workspace first. Write down what you expected to happen, what actually happened and what you changed when the result differed. This small habit becomes increasingly important as your AI tools gain access to more files and commands.

Do not move on until this is true.

The project passes the same scenarios after the fix and you have evidence of the test result.

Why we use this principle.

Nyfir Studios treats generated output and verified output as different states. Development work on local AI, Android software and bookkeeping workflows has repeatedly shown that recoverable state, explicit tests and clear product status are more useful than simply producing more output.