AI Code Review Guide
How to review AI-generated code, spot red flags, avoid the "looks right" trap, and build verification strategies that catch what AI gets wrong
Why AI Code Review Is Different
Reviewing human code and reviewing AI-generated code require fundamentally different skills. Human code has tells - inconsistent formatting, typos in comments, personal style quirks - that help reviewers calibrate their attention. When code looks sloppy, reviewers scrutinize harder. When it looks clean, they relax.
AI-generated code breaks this instinct. It is always well-formatted. Variable names are reasonable. Comments exist. The structure looks professional. This surface-level polish triggers a dangerous response: reviewers skim instead of reading, approve instead of questioning, and trust instead of verifying.
The result? Code that compiles, passes a glance test, and ships to production - carrying subtle bugs, architectural mismatches, and missing edge cases that no human developer would have written. This guide teaches you to see past the polish and catch what AI gets wrong.
The "Looks Right" Trap
The most dangerous property of AI-generated code is that it looks correct. It follows conventions, uses reasonable names, and produces output that seems right for simple inputs. The problems hide in the places you do not test: edge cases, error paths, concurrent access, and assumptions about your specific system.
What You See
- Clean formatting and consistent style
- Reasonable variable and function names
- Comments explaining the logic
- Error handling with try/catch blocks
- Correct output for simple test cases
What You Miss
- Null/undefined not handled in critical paths
- Race conditions in async operations
- Wrong assumptions about your data model
- Generic error handling that swallows specifics
- Logic that works for happy path only
A Real Example: The Plausible Sort
Consider this AI-generated function that sorts users by their last login date. It looks clean and passes basic testing:
function sortUsersByLastLogin(users) {
return users.sort((a, b) => {
return new Date(b.lastLogin) - new Date(a.lastLogin);
});
}
// Works great for: [{lastLogin: "2026-01-15"}, {lastLogin: "2026-02-01"}]
// Fails for: users with null lastLogin, invalid date strings,
// or lastLogin stored as Unix timestamps instead of ISO strings.
// Also mutates the original array (sort() modifies in place).function sortUsersByLastLogin(users) {
if (!Array.isArray(users)) return [];
return [...users].sort((a, b) => {
const dateA = a?.lastLogin ? new Date(a.lastLogin).getTime() : 0;
const dateB = b?.lastLogin ? new Date(b.lastLogin).getTime() : 0;
// Handle invalid dates (NaN from bad input)
const timeA = Number.isNaN(dateA) ? 0 : dateA;
const timeB = Number.isNaN(dateB) ? 0 : dateB;
return timeB - timeA;
});
}
// Returns a new array (no mutation)
// Handles null/undefined users, missing lastLogin, and invalid dates
// Users with no valid login sort to the endThe lesson: The AI version would pass any code review where the reviewer was skimming. It sorts correctly for well-formed data. But in production, users have null logins, accounts migrated from legacy systems have timestamps in different formats, and the array mutation causes bugs when the same list is used elsewhere.
Of developers report they review AI code less carefully than human-written code
Source: GitClear State of Software Quality 2025
Of AI-generated code contains subtle correctness issues that pass basic testing
Source: Stanford AI Code Study 2025
Security vulnerabilities found in unreviewed AI code compared to peer-reviewed human code
Source: Snyk AI Security Report 2025
Red Flags in AI-Generated Code
These eight patterns appear frequently in AI output. Train yourself to spot them during every review, and you will catch the majority of AI-introduced defects before they reach production.
Overly Verbose Error Handling
AI loves wrapping everything in try/catch blocks with generic console.error(err) messages. This catches errors but provides no useful context for debugging. It also silently swallows exceptions that should propagate to callers.
Look for: Catch blocks that log and continue, identical error messages across different functions, try/catch around code that cannot throw
Inconsistent Naming Within the Same File
AI models generate code token by token without a global view. One function might use userData while the next calls it userInfo or userObj. This signals the code was generated in separate chunks without a unified design.
Look for: Mixed camelCase and snake_case, the same concept called different names in different functions, abbreviations used inconsistently
Unused Imports and Dead Code
AI often includes imports for libraries it planned to use but ultimately did not, or generates helper functions that are never called. This bloats bundle sizes, confuses future developers, and sometimes pulls in dependencies with known vulnerabilities.
Look for: Import statements with no references, functions defined but never called, variables assigned but never read, commented-out code blocks
Functions That Do Too Much
AI tends to generate monolithic functions that fetch data, transform it, validate it, save it, and send notifications all in one block. These are impossible to unit test, hard to debug, and violate the single responsibility principle at every level.
Look for: Functions over 40 lines, multiple levels of nesting, functions with "and" in their description, parameters that control branching logic
Missing Edge Case Handling
This is the most common and most dangerous AI code flaw. AI trains on happy-path examples and generates code that works for typical inputs. It rarely accounts for empty arrays, null values, network timeouts, concurrent modifications, or malformed data from external sources.
Look for: No null checks on API responses, array access without length checks, division without zero guards, string operations without empty checks
Hardcoded Values That Should Be Config
AI frequently embeds URLs, port numbers, timeouts, retry counts, and feature flags directly in the code. These work in development but break across environments. Worse, AI sometimes invents plausible-looking URLs or API endpoints that do not exist in your infrastructure.
Look for: String literals for URLs or hosts, magic numbers (timeouts, limits, retry counts), environment-specific values embedded in logic
Generic Variable Names
Names like data, result, temp, item, and response appear constantly in AI output. They satisfy syntax requirements but tell you nothing about what the variable actually holds. This makes debugging and future maintenance significantly harder.
Look for: Variables named data, result, temp, item, obj, val, res, or single letters beyond loop counters
Comments That Restate the Code
AI generates comments like // increment counter above counter++. These add visual noise without adding understanding. Good comments explain why, not what. AI almost never explains the business reason behind a decision because it does not know it.
Look for: Comments that describe what the next line does, missing context about why a decision was made, JSDoc with obvious parameter descriptions
The AI Code Review Checklist
Run through these ten steps for every pull request that contains AI-generated code. Print this list, pin it to your monitor, and resist the urge to skip steps when the code "looks fine."
Read Every Line - Do Not Skim
Force yourself to read each line of AI code as if you wrote it and need to defend it in a review. If you catch yourself skipping ahead because it "looks standard," stop and go back.
Check Imports Against Actual Usage
Verify every import is actually used. Search the file for each imported name. Remove anything unused. Check that imported libraries are in your package.json and are not hallucinated packages.
Test with Null, Empty, and Extreme Inputs
For every function, mentally (or actually) pass in: null, undefined, an empty string, an empty array, zero, negative numbers, and absurdly large values. If the code does not handle these, it needs fixing.
Verify Error Handling Is Specific
Reject generic catch blocks. Each error handler should do something meaningful: retry with backoff, return a typed error, log with context, or escalate. If the catch block just logs and continues, it is hiding problems.
Check Naming Consistency
Scan the file for naming patterns. Is the same concept called different things in different places? Do naming conventions match the rest of your codebase? Inconsistency signals AI-generated chunks that were not unified.
Validate Against Your Architecture
AI does not know your architecture. Check that the code follows your patterns: correct layer for database access, proper use of your dependency injection, right error types for your error handling strategy, and correct use of your shared utilities.
Remove Restating Comments
Delete every comment that just restates what the code does. Replace them with comments explaining why - the business rule, the workaround reason, or the non-obvious constraint. If nothing needs explaining, no comment is needed.
Search for Hardcoded Values
Scan for string literals, magic numbers, and embedded URLs. Every environment-specific value should come from configuration. Verify that any URLs or API endpoints the AI used actually exist in your infrastructure.
Verify Tests Cover Edge Cases
If the AI generated tests, check that they do more than test happy paths. AI-generated tests often just verify that correct input produces correct output. Demand tests for error conditions, boundary values, and concurrent scenarios.
Apply the Explain-It Test
Can you explain every line to a teammate without saying "the AI wrote it"? If any section makes you pause, that section needs rewriting. You own every line that ships, regardless of who (or what) generated it.
Verification Strategies
Beyond the checklist, these four strategies form the foundation of effective AI code verification. Each targets a different failure mode that AI code commonly exhibits.
Read It Line by Line (Not Just Skim)
The most powerful verification tool is deliberate, slow reading. AI code is designed to look correct at a glance - that is its training objective. The bugs live in the details.
How to do it well:
- Read the function signature first - do the parameter types and return type make sense?
- Trace the data flow from input to output manually
- At each conditional, ask "what happens in the other branch?"
- For loops, consider: empty collection, single item, very large collection
Time investment: 5-10 minutes per function. This catches 60% of AI bugs that automated tools miss.
Test Boundary Conditions Manually
AI code passes typical inputs because that is what training data contains. The bugs appear at the boundaries: nulls, empties, maximums, negatives, and malformed data.
Boundary test inputs:
- null, undefined, NaN for every parameter
- Empty string, empty array, empty object
- Negative numbers, zero, Number.MAX_SAFE_INTEGER
- Strings with special characters, unicode, and SQL injection patterns
Time investment: 10-15 minutes per function. Write these as unit tests so the boundaries stay verified after future changes.
Check Against Your Architecture Docs
AI generates code that follows generic patterns, not your team's patterns. It will put database calls in controllers, skip your middleware chain, or use a different ORM pattern than your codebase uses.
Architecture checks:
- Does this follow your project's layer separation?
- Does it use your existing utility functions instead of reinventing them?
- Does it match your error handling strategy and error types?
- Does it integrate with your existing DI container and service registration?
Time investment: 5 minutes per file. Prevents the slow architectural drift that makes AI-heavy codebases increasingly inconsistent.
Ask "Could I Explain This to a Teammate?"
The most reliable verification method is also the simplest. If you cannot explain why the code works and why it was written this way, you do not understand it well enough to ship it.
Questions to answer:
- Why does this function exist? What problem does it solve?
- Why was this approach chosen over alternatives?
- What happens when this fails? How does the system recover?
- What assumptions does this code make about the data it receives?
Time investment: 2-3 minutes per function. If you cannot answer these questions, the code needs to be rewritten until you can.
Team Review Process for AI-Heavy Codebases
Individual code review skills matter, but the real protection comes from team-level processes. These practices adapt your PR workflow for the reality that significant portions of new code now come from AI assistants.
Label AI-Generated Code in PRs
Require developers to tag PRs or specific files that contain AI-generated code. Use labels like ai-assisted or copilot-generated. This is not about blame - it signals to reviewers that different scrutiny is needed. AI code has different failure modes than human code, and reviewers need to know which lens to apply.
Implementation: Add PR template checkboxes, GitHub labels, or a commit message convention like [ai-assisted] prefix. Some teams track the AI-to-human code ratio over time.
Require Boundary Tests for AI Functions
Make it a team rule: every AI-generated function must ship with tests for null input, empty input, and at least one error condition. AI-generated tests alone are not sufficient because they typically only test the happy path the AI was thinking about when it wrote the function.
Implementation: Add a PR checklist item: "Boundary tests written (not AI-generated) for new functions." Enforce minimum test coverage for files flagged as AI-assisted.
Rotate Reviewers to Prevent Familiarity Bias
When the same person always reviews the same developer's AI-assisted PRs, they build a false sense of trust. Rotating reviewers brings fresh eyes that are more likely to question assumptions and catch patterns the regular reviewer has stopped noticing.
Implementation: Use CODEOWNERS rotation, automated reviewer assignment, or a simple round-robin schedule. Ensure at least one reviewer per quarter who has not reviewed that area before.
Monthly Architecture Consistency Reviews
AI code drifts from your architecture one PR at a time. Each individual change looks reasonable, but over months the codebase develops multiple competing patterns for the same operations. Schedule monthly reviews to catch this drift before it becomes entrenched.
Implementation: Monthly 30-minute team review of AI-heavy modules. Compare patterns against your architecture decision records (ADRs). Flag and consolidate competing patterns in the next sprint.
Track AI Code Bug Rates Separately
Measure whether AI-generated code has a different bug rate, revert rate, or time-to-fix than human-written code. This data helps you calibrate how much review overhead is justified and where your team's AI usage is creating the most risk.
Implementation: Tag bugs found in AI-labeled PRs. Track metrics: bugs per AI-assisted PR vs. human-only PR, median time to discover, and production incident correlation. Review quarterly.
Frequently Asked Questions
AI-generated code is syntactically correct and well-formatted, which tricks reviewers into skimming. Unlike human code with obvious typos or style issues, AI code fails silently through missing edge cases, incorrect assumptions about your architecture, and plausible but wrong logic. Reviewers must actively resist the "looks right" bias and apply deliberate, line-by-line scrutiny to catch the subtle issues that AI introduces.
The top red flags include overly verbose error handling that catches everything generically, inconsistent naming conventions within the same file, unused imports and dead code paths, functions doing too many things at once, missing edge case handling for nulls and empty arrays, hardcoded values that should be configuration, generic variable names like data and result, and comments that restate what the code already says instead of explaining why.
Build AI-specific review shortcuts: create a team checklist targeting known AI weaknesses, use linters configured for AI-common issues like unused imports and generic catch blocks, require boundary condition tests for AI-generated functions, and train the team to spot the "looks right" trap. These focused checks add five to ten minutes per PR but prevent hours of debugging in production. The net effect is faster delivery, not slower.
Yes. Labeling AI-generated code in PRs helps reviewers apply appropriate scrutiny. Many teams use tags like ai-generated or copilot-assisted to signal that extra verification is needed. This is not about blame or gatekeeping - it is about adjusting the review lens because AI code has different failure modes than human-written code. Teams that label see 30-40% fewer AI-related bugs in production.
No. AI review tools catch syntax issues and common patterns but miss architectural mismatches, business logic errors, and context-dependent problems. They complement human review by handling mechanical checks - unused imports, formatting, simple code smells - which frees humans to focus on design, correctness, and maintainability. The best approach combines automated tools for fast feedback with human reviewers for judgment calls.
Four strategies stand out: read every line instead of skimming (catches 60% of bugs automated tools miss), test boundary conditions manually with edge case inputs, verify the code matches your architecture conventions and patterns, and apply the explain-it test by asking whether you could explain every line to a teammate. If any step reveals confusion or uncertainty, the code needs revision before it ships.
Master AI-Assisted Development
AI code review is just one piece of the puzzle. Learn how AI coding patterns create debt and where AI-generated tests fall short.