AI-Generated Code: Verify Before Deployment

A form may accept correct data but fail as soon as a field is empty. Seeing a demonstration work is therefore not enough to integrate AI-generated code. You must establish the expected behavior, examine changes, and verify situations that can actually occur.

This method is aimed at entrepreneurs and teams who have a script, page, or small function prepared with AI. It helps organize work with a competent developer. Generation does not replace technical review or testing and does not guarantee the security of the result.

1. Describe the Requirement as a Verifiable Contract

Start with input, output, and rules. "Create an order form" is too vague. Specify the expected fields, accepted values, error messages to display, and allowed operations. Also indicate what the code must not modify.

In a fictional example, a shop wants to limit the requested quantity to an integer between 1 and 12. The text "2.5", a negative value, and an empty field must be rejected with a clear message. These numbers serve only to illustrate a business rule, not to recommend a universal limit.

Note your application's constraints: language, conventions, existing components, and runtime environment. If you ignore them, have the project reviewed before requesting a solution. A standalone snippet may seem correct while being incompatible with the system that must host it.

Anticipate Acceptance Criteria

Write examples before generation: input, expected result, and justification. For a quantity function, the normal case, the two limits, an out-of-range value, and a non-numeric value suffice to clarify several ambiguities. Final tests will then depend on risk and integration.

2. Request a Limited and Explainable Modification

Prefer a circumscribed task over a complete overhaul in a single prompt. Provide the necessary code after removing secrets, credentials, and private data. If the code tool is available in your CREZOVA space, use it to prepare a proposal you can review in your usual environment.

Prepare a limited modification for the quantity validation described below. Respect the provided language, conventions, and components. Before the code, list your assumptions and missing information. Do not add any dependency without explaining its necessity. Do not modify payment or pricing rules. Provide: code proposal, explanation of changes, test cases, and points requiring human review. Fictional demonstration rule: integer between 1 and 12 inclusive, other inputs rejected. Context: [useful snippet without secrets]. Environment: [verified information].

Request an explanation of the choices, then compare it to the code. A convincing explanation does not prove that the announced behavior exists. If the model adds network access or a library unrelated to the need, have it justify or remove that addition.

3. Review Changes in Their Context

Examine the difference between the previous version and the proposal. Check modified files, called functions, and effects on data. A local fix can change behavior shared elsewhere in the application.

For a form, do not settle for the visible control in the browser. Have the developer verify that the necessary rules are also applied where the application actually processes the request. An interface can be bypassed or receive unexpected inputs.

Dependencies, permissions, file access, and database operations deserve special attention. The OWASP Top 10 repository presents major web application risks. It helps guide a specialized review but does not alone constitute a complete audit or proof of security.

4. Test Useful Behaviors in an Isolated Environment

Work on a copy or a suitable development environment. Use demonstration data and avoid any unintended connection to real payments or messages. Save the useful state before an operation that modifies data.

Review your acceptance criteria and execute the cases. Check the obtained result, not just the absence of errors. In our fictional example, verify that the rejected quantity is not recorded despite the displayed message. Also verify that correct input still follows the expected path.

Add tests that protect integration: display on targeted screens, exchanges with the server, user rights, and errors from the called service. The level of review should correspond to what the code can affect. Payment processing requires more guarantees than a presentation change.

5. Prepare a Delivery That the Team Can Take Over

Keep a short note: initial requirement, changed files, assumptions, executed tests, and known limitations. Another person must be able to understand what is validated. Have the changes approved by the technical lead before deploying them according to your procedure.

Plan how to revert to the previous version and how to observe behavior after going live. A code rollback does not automatically fix data already modified: operations that write or delete information require specific preparation.

If the code uses commercial content, also verify reference data. A technically correct rule can apply a wrong price or an invented feature. The method of preparing a verified product sheet helps ensure these entries.

• Inputs, outputs, and business rules are explicit.
• The modification remains understood and limited to the need.
• Dependencies and sensitive operations have been examined.
• Tests cover useful cases and their real effects.
• A competent person validates integration and delivery.

Complete the documentation with your process for controlling generated content. Check the tools available in your space to prepare a precise code request and its acceptance criteria.

Frequently Asked Questions

Does the fact that the code runs mean it is ready?

Its behavior, integration, and effects still need to be verified. Successful execution on one case does not demonstrate robustness or security of the whole.

Can the same AI be asked to verify its own code?

This second reading can provide leads. It does not replace executed tests or competent human review, especially when the code touches accounts, payments, or data.