A Practical Acceptance-Test Contract for AI-Built CRUD Apps
AI can produce a convincing interface before it has proved that the underlying workflow works. A better starting point is a small acceptance-test contract: a list of observable behaviors that must remain true before you call the project useful. This is not a demand for exhaustive quality assurance. It is a way to turn an idea into one complete, inspectable path. Example: a customer-inquiry…
The article advocates for a practical approach when using AI to create simple CRUD applications. Instead of expecting perfect results upfront, it suggests defining a minimal set of acceptance tests to verify that the core workflow functions correctly.
The authors provide a detailed checklist for a hypothetical customer inquiry tracking system. It lays out the happy path scenario, where submitting a fully filled form should create a new record with the expected status. It also defines a recoverable failure case where an incomplete form should not silently save invalid data but instead show a clear error message while preserving any existing information so the user can retry.
Another key point is the importance of persistence. The article stresses that a prototype must survive a page refresh or reopening the view without losing data. This simple persistence test helps separate a convincing but ultimately temporary demo from a true usable workflow.
The authors go further to argue that a preview can look good but still be unreliable if its data only exists in memory. They recommend refreshing the page or reopening the application to confirm the state persists. This ensures the preview genuinely reflects the final product's behavior.
Additionally, the article stresses that the preview must demonstrate the real workflow, not just a polished representation. It also cautions about failing to verify access controls and tenant separation when dealing with multiple users. These are crucial considerations beyond the basic acceptance contract.
The authors provide an example build request that includes the acceptance tests and what the developer should report back. This structured approach allows stakeholders to review the evidence and decide whether the minimum viable product meets the necessary criteria to proceed.
Ultimately, the article defines a compact "definition of done" for a small AI-built workflow. It validates that valid inputs succeed, invalid inputs fail clearly and can be recovered from, important state survives refresh, the preview accurately demonstrates the real workflow, and the developer states what was not verified. This focused approach ensures the project stays manageable while still proving core functionality before expanding the scope.
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