AI-codering

The output is a proposed implementation that needs the same attention to requirements, behavior, security, and maintainability as other code. Plausible syntax and a confident explanation do not establish correctness.

Key takeaways

• Provide requirements and repository context.
• Verify APIs and dependencies.
• Test behavior and inspect the final change.

Give the system the relevant context: the problem, existing architecture, interfaces, constraints, and examples of expected behavior. A solution that compiles can still solve the wrong problem or conflict with repository conventions. Review dependencies and API assumptions. Models can suggest nonexistent functions, outdated interfaces, or packages whose purpose and provenance have not been checked. Use current official documentation and inspect the code that will actually run. Test behavior with meaningful cases, including boundaries and failures. A test that merely reproduces the implementation’s assumptions can pass while the requirement remains unmet. For a bug fix, include evidence that the original failure is corrected without removing the test or weakening its expectation. Keep changes reviewable and verify the final artifact. Examine diffs for unrelated edits, sensitive data, destructive operations, and missing error handling. If the code changes a user interface or external workflow, inspect the rendered or operational result as well as running automated checks.

1. Imagine generated JavaScript sorting the numbers 2, 10, and 1 without a numerical comparator.
2. The default string-based ordering can produce 1, 10, 2 rather than the required numerical order.
3. Test varied values and define the intended ordering explicitly before accepting the function.

The constructed example shows why a short, valid-looking function still needs behavioral checks.