Evaluate AI Coding Suggestions Before Shipping

AI coding suggestions can speed up development, but plausible-looking code may still be incorrect, insecure, or incompatible with your project. Use this review process to verify what a suggestion does, test its behavior, and decide whether it is safe to merge or release. Before you begin, make sure you can inspect the relevant files, run the project’s checks, and compare your changes with the original code.

What You’ll Need

• The proposed code and the surrounding files it depends on.
• A local development environment with the project’s usual build, test, and lint commands.
• Access to the project’s requirements, documentation, and code review process.
• A way to review the change as a diff and, for risky work, a separate test environment.

Step-by-Step

1. Restate the requirement. Write down what the change must do, which inputs and outputs it affects, and any constraints such as supported platforms, performance, accessibility, or compatibility. Treat the requirement—not the suggestion’s explanation—as the standard for judging the code. If the request is ambiguous, clarify it before accepting an implementation.
2. Inspect the full diff. Review every changed line, including generated files, configuration, dependencies, and tests. Check for unrelated edits, removed checks, unexpected logging, hard-coded values, or changes that were not requested. A small suggested function may have effects elsewhere through shared state, APIs, database changes, or altered defaults.
3. Trace the logic yourself. Follow the data from its source through the proposed code to its result. Check assumptions about types, null or missing values, empty collections, boundaries, time zones, rounding, and error handling. Compare the implementation with established project patterns and the relevant library or language documentation. Do not rely on comments or a confident explanation as proof that the behavior is correct.
4. Check security and privacy implications. Look for unsafe handling of user input, authorization gaps, exposed secrets, overly broad permissions, insecure file or network operations, and sensitive data written to logs. Confirm that validation and access checks happen in the right place. If the change touches authentication, encryption, payments, personal data, or other sensitive areas, ask an appropriate reviewer to examine it rather than relying on a quick visual check.
5. Verify dependencies and licensing. If the suggestion adds or changes a package, check that the package exists, is maintained enough for your needs, fits the project’s supported versions, and is permitted by your organization’s policies. Inspect lockfile changes and transitive additions. Avoid copying unfamiliar code without checking its source and license requirements.
6. Write or review focused tests. Add tests for the required behavior, not just for the code’s current output. Include normal cases, edge cases, invalid input, and failure paths where relevant. For a bug fix, add a test that would have failed before the fix. Tests should be understandable and should not simply reproduce assumptions made by the suggestion.
7. Run the project’s checks. Start with focused tests, then run the standard test suite, formatter, linter, type checker, and build steps used by the project. Read failures rather than dismissing them as unrelated. A passing test suite is useful evidence, but it cannot establish correctness for behavior the tests do not cover.
8. Check behavior in context. Run the application or relevant feature in a local or staging environment when practical. Try representative inputs and failure conditions, and inspect logs or outputs for unexpected behavior. For changes involving data formats, APIs, or databases, check compatibility with existing callers and stored data before release.
9. Review maintainability and release impact. Ask whether another developer can understand the code, whether it duplicates an existing utility, and whether errors can be diagnosed later. Consider migrations, rollback, feature flags, monitoring, and deployment order if the change affects production behavior. Remove temporary debugging code and unnecessary edits.
10. Make an explicit ship decision. Merge only when the change meets the requirement, the relevant checks pass, risks have been reviewed, and unresolved uncertainties are documented and handled. If you cannot establish what a line does, reproduce a failure, or verify a critical assumption, pause and ask for human review. The author of the suggestion does not replace the project’s normal approval process.

Common Mistakes

• Accepting code because it compiles. Compilation catches some errors, not incorrect business rules, missing authorization, or bad edge-case behavior.
• Reviewing only the suggested snippet. Imports, configuration, dependency updates, and surrounding call sites can change the actual impact.
• Testing only the happy path. Empty input, timeouts, malformed data, and permission failures often reveal problems that normal examples miss.
• Trusting generated tests without reading them. A test can pass while checking the wrong requirement or repeating the implementation’s mistake.
• Ignoring project conventions. A technically valid approach may conflict with established error handling, compatibility, or data-access patterns.
• Shipping under time pressure without recording uncertainty. Unverified assumptions should be resolved or clearly escalated, not hidden by a clean test run.

Tips

• Ask for a small, reviewable change rather than a broad rewrite. Smaller diffs make cause and effect easier to inspect.
• Use a clean working tree or a separate branch so you can identify exactly what changed and revert it safely.
• Compare behavior against a concrete example or acceptance criterion, especially when the code handles dates, money, rounding, or external formats.
• Keep tests independent of implementation details where possible; assert useful behavior rather than private function structure.
• Use automated security and dependency checks as additional signals, not as substitutes for review.
• For high-impact changes, request review from someone familiar with the affected system and document the test results and known limitations.

Troubleshooting

The code does not build or tests fail: Reproduce the failure on the unchanged baseline if possible, then inspect the first meaningful error and identify whether the suggestion introduced it. Check expected tool versions and project setup before changing code to silence a failure. Do not delete or weaken a test just to get a green run.

The output looks right, but correctness is uncertain: Create a minimal example with a known expected result, then trace each transformation. Consult the project’s authoritative requirements or library documentation. If the expected result is not defined, ask the product or system owner to clarify it.

A dependency or API cannot be verified: Check the package registry or official project documentation, the version constraints, and the lockfile. If the source or compatibility is unclear, remove the addition or ask a maintainer before proceeding.

A security concern appears: Stop the release path for that change, preserve the relevant diff, and involve the team responsible for security or the affected system. Avoid exposing secrets or sensitive data while sharing examples. Do not treat a passing scanner as clearance for a concern you have identified.

Checks pass locally but behavior differs in staging: Compare configuration, data shape, permissions, and dependency versions between environments. Reproduce with safe test data, capture the specific difference, and do not promote the change until the discrepancy is understood.

Frequently Asked Questions

Should every AI coding suggestion be treated as untrusted?
Yes, in the practical sense that it needs the same verification as code from any source. That does not mean every suggestion is wrong; it means evidence should determine whether it is acceptable.

Are passing tests enough to ship?
No. Tests cover only the cases they exercise. Review the diff, security implications, compatibility, and project requirements as well.

How many tests should I add?
Add tests that cover the requirement and meaningful boundary or failure cases. There is no universal count; prioritize coverage of distinct behaviors and risks.

Can I ship code if I do not understand every line?
Do not ship a change with unexplained behavior that could affect correctness, security, or reliability. Ask for clarification or review, or replace the unclear part with an approach you can verify.

What if the suggestion changes many files?
Break the work into smaller changes if possible. Review each part independently, identify generated or dependency files, and confirm that every edit is necessary for the requirement.

Does human review guarantee the code is safe?
No review guarantees that. Independent review adds another chance to find issues, but it should be combined with tests, automated checks, and appropriate monitoring or rollback planning.

Related

• AI Productivity as a Business Growth Strategy
• Threadripper 3990X: Did It Launch at CES?
• Get Last Name From Name? A Clear Guide