Learning how to review AI generated code means checking what it does, how it does it, and whether it fits the project—not guessing who wrote it.
A careful review can also distinguish code quality checks from tools that estimate AI authorship or detect copied code.
Start with requirements and context
Before reading individual lines, identify the change’s purpose.
Compare the code with the issue, specification, or user story, and note the expected inputs, outputs, error handling, and constraints.
If the request is unclear, ask for clarification before approving a solution that may be technically sound but solve the wrong problem.
Inspect the surrounding project as well.
Read nearby functions, tests, configuration, and conventions.
AI-generated code may use plausible patterns that conflict with the application’s architecture, supported language version, or existing interfaces.
Check whether it introduces a new dependency, changes public behavior, or duplicates a capability already available in the codebase.
Check behavior, edge cases, and failure paths
Trace the code with representative inputs rather than relying on descriptive comments.
Follow how data enters the function, changes, and leaves it.
Confirm that the implementation handles normal cases and foreseeable edge cases, including empty values, malformed input, boundary values, timeouts, and unavailable services where relevant.
- Check that conditions cover the intended cases and do not accidentally skip or repeat work.
- Look for off-by-one errors, incorrect defaults, timezone assumptions, and unsafe type conversions.
- Verify that errors are handled deliberately, surfaced at the right layer, and not silently discarded.
- Confirm that cleanup occurs when operations fail, especially when files, connections, locks, or transactions are involved.
- Check that asynchronous work is awaited, cancellation is considered, and shared state is updated safely.
Pay special attention to code that appears unusually elaborate for a simple requirement.
Extra abstractions, fallback branches, or broad exception handlers can hide faulty assumptions.
Ask what each part contributes and whether a simpler implementation would be easier to verify.
Review security and data handling
Review generated code with the same security expectations as any other contribution.
Identify trust boundaries: user input, external APIs, files, databases, environment variables, and third-party packages.
Check that input is validated for the operation being performed and that output is encoded or parameterized appropriately.
Watch for hard-coded credentials, tokens in logs, overly broad permissions, unsafe deserialization, shell commands built from untrusted strings, and database queries assembled through string concatenation.
For web code, inspect authentication and authorization separately: being signed in does not necessarily mean a user is allowed to access a particular record or action.
Review dependencies and configuration changes too.
Confirm that a package is necessary, its name is correct, and its use follows the project’s dependency and license practices.
Do not assume a familiar-looking package name or API call is valid; check the project’s trusted documentation or established usage in the repository.
Evaluate tests and verify the change
Tests should demonstrate the behavior required by the change, not merely repeat the implementation’s assumptions.
Read new tests for meaningful assertions, representative cases, and failure coverage.
A test that only checks that a function runs may pass while the function returns the wrong result.
Use the project’s normal verification steps, such as targeted tests, type checks, linting, or a build, when available.
Review the diff after running them and investigate failures rather than dismissing them because the code came from an AI assistant.
Automated checks provide evidence about specific conditions; they do not replace understanding the change or reviewing its interaction with the rest of the system.
Where practical, compare expected and actual behavior with a small example or a focused test.
If the change affects a critical path, request review from someone familiar with that part of the system.
Record any assumptions that remain unresolved so they can be addressed before release.
Assess readability and maintainability
Good code should make its intent reasonably clear to the next person maintaining it.
Look for names that match the domain, focused functions, consistent formatting, and comments that explain decisions rather than restating syntax.
Check whether the implementation follows local conventions and whether its complexity is justified.
AI assistants can produce confident but inaccurate comments, stale examples, or references to APIs that do not exist in the project’s version.
Verify claims against the actual code and documentation.
Remove misleading comments and avoid retaining generated boilerplate that obscures the behavior.
Understand authorship detection and similarity checks
Reviewing quality and determining authorship are different tasks.
An AI authorship classifier estimates whether text or code resembles material produced by a model; a code similarity or plagiarism check looks for overlap with other source material.
Neither kind of result establishes, by itself, who wrote a contribution or whether rules were broken.
Scores should be treated as signals with limitations, not proof.
A detector can produce false positives or miss AI-assisted work, and results may be affected by code length, language, formatting, or editing.
Turnitin states that its AI writing model does not reliably detect code, so its writing report should not be confused with source-code similarity analysis.
Codespy is an external source-code detector that may be considered when a code-focused screening step is relevant.
Treat any result as one input for further review, not an endorsement, definitive attribution, or substitute for examining the code and its provenance.
Product claims, capabilities, and terms should be checked directly with the provider before relying on them.
Follow the applicable policy and document the review
Rules for AI assistance differ across employers, schools, clients, and open-source projects.
Check the policy that applies to the specific work, including requirements for disclosure, approved tools, data handling, licensing, and human verification.
Do not assume that one organization’s rules apply to another.
Keep the review focused on observable evidence: requirements met, tests run, risks found, and changes requested.
If authorship matters under a stated policy, use the process that policy specifies and avoid making accusations from a detector score alone.
A clear record of technical findings helps maintain accountability without overstating what a tool can establish.