Build an AI workflow
Use a repeatable plan → implement → test → inspect → fix → retest loop.
What this step is for.
Reliable AI work is a loop, not a single prompt. Plan what will change, implement a bounded step, run checks, inspect output, fix failures and retest. The test result—not the assistant’s confidence—determines whether a step is complete.
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.
Write a six-stage checklist: Plan, Implement, Build, Test, Inspect, Retest. Use it for one change in your practice project and save the result of each stage.
Run the loop once end to end
Use the tiny project from Step 6. Plan one change: “Add a Reset button.” Ask AI to implement only that change. Inspect the diff, open the project, test both the existing button and Reset, record PASS/FAIL, fix any failure and retest. The workflow is complete only when the planned behavior works and previous behavior still works. Save a Git commit after verification.
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.
Every stage has an observable output and a failed test sends the work back to implementation rather than being ignored.
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.