AI coding assistants can speed up implementation, but generated code still needs the same careful review as code written from scratch. This reusable AI-generated code review checklist helps you verify behavior, security, dependencies, tests, performance, maintainability, licensing concerns, and unsupported APIs before merging an AI-assisted pull request.
Overview
AI-generated code is best treated as a draft from a fast but fallible collaborator. It may produce a useful first implementation, yet it can also misunderstand requirements, invent library methods, copy an unsuitable pattern, omit error handling, or create tests that confirm the implementation rather than the intended behavior. The reviewer remains responsible for deciding whether the change is correct and appropriate for the project.
A reliable review starts before you inspect individual lines. Read the issue, acceptance criteria, related documentation, and pull-request description first. Identify what the change is supposed to do, what it must not do, and which systems it can affect. Then review the generated diff in small logical units rather than accepting a large assistant-produced commit as one block.
Use this sequence for most AI-assisted changes:
- Understand the requirement: Restate the expected inputs, outputs, constraints, and failure behavior.
- Inspect the diff: Look for unrelated edits, surprising abstractions, and changes outside the requested scope.
- Verify behavior: Run tests and manually check important paths, including invalid and boundary inputs.
- Review risk: Examine security, data handling, dependencies, performance, and operational impact.
- Improve the code: Remove unnecessary complexity and document decisions that future maintainers need to understand.
For repository workflow fundamentals, see this Git and GitHub workflow for beginners. A clean branch, focused pull request, and safe rollback path make AI-assisted changes easier to evaluate.
Checklist by scenario
The depth of review should match the potential impact of the change. Use the following checklist as a starting point, then involve an appropriate subject-matter reviewer for sensitive systems.
| Scenario | Primary risks | Review emphasis |
|---|---|---|
| Small refactor | Behavior changes hidden by renamed or moved code | Compare outputs before and after, preserve public interfaces, and check whether tests cover the affected paths. |
| Bug fix | Fixing one case while breaking another | Reproduce the original failure, add a regression test, and test nearby boundary conditions. |
| New API or endpoint | Weak validation, authorization gaps, information leakage | Review authentication, authorization, input limits, error responses, logging, and rate or resource controls where relevant. |
| Dependency or configuration change | Untrusted packages, incompatible versions, unsafe defaults | Confirm the package and version exist, inspect its purpose, run the project’s dependency checks, and review configuration for each environment. |
| Database or query change | Incorrect results, injection, slow queries, migration failures | Check parameterization, indexes, transaction behavior, rollback planning, and realistic data volume. The SQL query optimization checklist can help with performance-focused review. |
| Generated test suite | Tests that mirror faulty code or miss important cases | Verify that assertions express requirements, not just execution. Add negative, boundary, permission, and failure-path tests. |
| Security-sensitive code | Exposure of secrets, unsafe parsing, injection, access-control errors | Use a second reviewer when appropriate, test misuse cases, and avoid treating an assistant’s security explanation as evidence. |
For JavaScript changes, pay particular attention to asynchronous control flow, rejected promises, cancellation, and inconsistent error handling. This JavaScript async programming guide provides useful review context. For request and response debugging, the API debugging toolkit can complement automated tests.
What to double-check
Correctness and requirements
- Does the implementation solve the stated problem rather than a similar one?
- Are null, empty, malformed, duplicate, oversized, and unexpected inputs handled intentionally?
- Are time zones, units, ordering, encoding, rounding, and case sensitivity explicit where they matter?
- Does the code preserve existing public behavior and backward compatibility?
Hallucinated APIs and dependencies
Never assume that an imported function, configuration option, command, or framework feature exists because the generated code uses it confidently. Search the installed package, inspect type definitions, read the relevant project documentation, and run a minimal example. Confirm method names, argument order, return types, supported versions, and error behavior. Remove a dependency if a small local implementation or an existing project utility is safer and clearer.
Security and data handling
Check every trust boundary. Look for unsanitized input reaching queries, shell commands, templates, file paths, redirects, or HTML. Verify authorization at the point where protected data is accessed, not only in a user interface. Confirm that secrets are not embedded in source, prompts, test fixtures, logs, or generated documentation. Review error messages and telemetry for accidental disclosure. For encoded tokens or request data, use a decoder or formatter only as a diagnostic aid; decoding is not the same as validating authenticity.
Tests, performance, and operations
Run the project’s formatter, linter, type checker, unit tests, integration tests, and relevant security or dependency checks. Add a test that would fail if the original bug returned. Consider repeated database calls, unbounded loops, large payloads, unnecessary network requests, memory growth, and concurrency behavior. Check observability as well: can an operator identify a failure without logging sensitive values?
Maintainability and licensing
Ask whether a teammate can explain the code six months from now. Prefer local conventions over novel patterns, short functions over clever compression, and meaningful names over comments that merely restate syntax. Preserve attribution and follow the repository’s rules for third-party code. If generated code resembles an external snippet or introduces copied material, pause and verify that its use is acceptable under the project’s applicable policies.
Common mistakes
- Reviewing the explanation instead of the implementation: A plausible summary does not prove that the code works.
- Accepting generated tests as coverage: Tests can be numerous while missing the requirement, asserting weak conditions, or repeating the same mistaken assumption.
- Ignoring unrelated changes: Ask for a smaller pull request when formatting, renaming, and feature work are mixed together.
- Trusting familiar-looking security code: Authentication, authorization, cryptography, and input validation need explicit verification and, where appropriate, specialist review.
- Sharing sensitive context casually: Before using an assistant, understand the repository’s approved workflow, access settings, retention choices, and restrictions. Do not paste secrets or private customer data into prompts.
- Skipping manual checks because automation passes: Automated checks are evidence, not a complete specification. Exercise important user and failure flows directly.
For utility-heavy work, compare generated solutions with established developer tools rather than adding complexity unnecessarily. A JSON formatter, regex tester, or API inspection tool can help validate assumptions, but each result still needs interpretation. See the developer tools toolkit for examples of where these utilities fit into a review workflow.
When to revisit
Use this checklist before every AI-assisted merge, especially after a refactor, bug fix, generated test suite, dependency update, or change to authentication, payments, personal data, infrastructure, or public APIs. Revisit the team’s process before seasonal planning or release cycles, when repository permissions change, or when the coding assistant, model, editor integration, or supported workflow changes.
Keep a lightweight pull-request record so reviewers can see what was generated, what was manually changed, which tests were run, and which assumptions were verified. A practical review comment can follow this template:
Purpose: What requirement does this change satisfy?
AI assistance: Which parts were generated, suggested, or rewritten?
Verification: Which tests, manual checks, documentation lookups, and dependency checks were completed?
Risk review: What security, data, performance, compatibility, and operational risks were considered?
Open questions: What still needs a human decision or specialist review?
Finally, do not merge until you can explain the change without relying on the assistant. That standard preserves the main benefit of AI pair programming—faster exploration—while keeping correctness, security, and long-term ownership with the development team.
