Debugging AI-generated code with checklists

AI-generated code fails in predictable ways: logic errors the model confidently asserts are correct, edge cases silently ignored, and dependencies imported from packages that don't exist. The traditional fix—reading logs, stepping through a debugger, adding print statements—works, but wastes time on bugs that shouldn't have shipped in the first place. A structured checklist run before you commit catches these failures faster than any post-mortem ever will.

This guide walks through checklists that catch AI code bugs at review time, organized by error type: syntax and runtime issues, logic flaws, missing edge cases, security holes, and performance traps. Each checklist item includes what to look for and how to verify it, so you can adapt the list to your stack and ship AI-assisted work with confidence.

Why AI-generated code needs different debugging tactics

Human developers make mistakes, but AI models produce a distinct failure profile. They hallucinate APIs that almost exist, write syntactically valid code with broken logic, and skip error handling because the training data often omits it. Traditional debugging—waiting for a crash, reading the stack trace, fixing the line—works, but it's reactive. You've already merged the bug, deployed it, or wasted a reviewer's time.

Checklists shift the work earlier: you catch hallucinated imports before the build fails, verify edge cases before the unit test suite runs, and confirm auth checks before security flags it. The cost is a few minutes of manual scanning; the payoff is fewer reverts, faster PR cycles, and less firefighting in production.

Checklist: syntax, imports and runtime errors

Start with the errors that break builds or crash immediately. AI models are trained on massive corpora of working code, so they're decent at syntax—but they confidently invent plausible-sounding imports and method signatures.

• Imports and package names: Does every import or require reference a package that actually exists? Check package.json, requirements.txt, or go.mod. AI often invents names like stripe-payments when the real package is stripe.
• Method signatures: Does each method call match the library's current API? AI training data lags, so it may use deprecated arguments or outdated method names. Consult the official docs for any library you don't recognize.
• Type errors: If you're in TypeScript, Python with type hints, or Go, does tsc --noEmit, mypy, or the compiler pass cleanly? AI sometimes mixes string and number, or returns Promise<void> when the signature expects Promise<User>.
• Environment variables: Does the code reference process.env.API_KEY or os.getenv("SECRET") without documenting where to set it? Check that every env var is either in .env.example or the deployment guide.

Run these checks before you run the code. A two-minute scan saves ten minutes of "module not found" debugging.

Checklist: logic and business rules

Syntax-correct code can still implement the wrong behavior. AI models predict tokens, not intent, so they'll write a loop that technically runs but produces incorrect results.

• Boundary conditions: Does the code handle zero, one, and many items? For example, a batch processor that assumes items.length > 0 will crash on empty input.
• Null and undefined: Are all optional fields checked before access? Look for user.profile.name without confirming user.profile exists.
• Off-by-one errors: Do array indices, slice ranges, and loop conditions match the intended range? AI often writes for i in range(len(arr)) then indexes arr[i+1].
• Business logic: Does the code actually solve the requirement? Read the function name and docstring, then trace the logic. AI sometimes writes code that looks relevant but calculates the wrong metric or updates the wrong database field.

For example, if the task is "calculate 30-day active users," verify the date math spans exactly 30 days, not 29 or 31, and that the query counts distinct users, not sessions.

Checklist: edge cases and error handling

AI models learn from happy-path examples, so they routinely omit error handling, input validation, and retries.

• Input validation: Does the function validate arguments? Check for missing null checks, type assertions, or range constraints. Example: a setDiscount(percent) function should reject values outside 0–100.
• Network and I/O failures: Does code that calls external APIs or databases handle timeouts, retries, and HTTP error codes? AI often writes response = requests.get(url) with no try-except.
• Concurrency: If multiple requests can run concurrently, are shared resources protected? Look for race conditions in counters, file writes, or database updates without transactions.
• Resource cleanup: Do file handles, database connections, and network sockets close in all code paths, including exceptions? AI forgets finally blocks or context managers.

Add a manual test: call the function with invalid input, simulate a network failure, or trigger an exception mid-execution. If it crashes ungracefully or leaves resources open, add the missing guards.

Checklist: security and permissions

AI training data includes public GitHub repos, which are full of hardcoded secrets, SQL injection vulnerabilities, and missing auth checks. Don't assume generated code is secure.

• Hardcoded secrets: Does the code contain API keys, passwords, or tokens? Search for patterns like api_key = "sk_live_..." or password = "admin123".
• SQL injection: Are database queries parameterized? Any string concatenation like f"SELECT * FROM users WHERE id = {user_id}" is a vulnerability.
• Auth and permissions: Does the endpoint or function check that the current user is allowed to perform the action? AI often omits if user.role != 'admin': raise Forbidden().
• Data leakage: Does the response include fields the user shouldn't see? Look for serializers or JSON responses that dump entire objects without filtering sensitive columns.

If your stack includes frameworks with built-in protections—Django's ORM, prepared statements, middleware auth—verify the AI used them instead of writing raw queries or skipping the auth layer.

Checklist: performance and scalability

AI-generated code often works fine in development with ten records and falls over in production with ten thousand.

• N+1 queries: Does the code loop over a collection and make a database or API call inside the loop? Example: for user in users: fetch_orders(user.id) instead of a single join.
• Unbounded operations: Are there limits on batch sizes, pagination, or result counts? A function that loads SELECT * FROM logs without a LIMIT will exhaust memory.
• Expensive computations in loops: Does the code recalculate the same value inside a loop? Example: for item in items: if expensive_function() > threshold: instead of calling expensive_function() once before the loop.
• Missing indexes: If the code queries by a new field, is there a database index? AI doesn't create migrations for indexes.

Profile or benchmark any function that processes user input or handles batch operations. If it scales linearly with input size when it should be constant or logarithmic, refactor before merging.

How AI coding agents fit into the checklist workflow

Platforms like Goatfied use cloud coding agents that test their own changes and open pull requests. The GOAT agents can be prompted to run subsets of these checklists automatically—validate imports, run type checkers, confirm tests pass—before they submit code for review. You still need human judgment for business logic and security, but automating the mechanical checks (syntax, imports, basic error handling) shrinks the manual checklist to five or six high-value items instead of twenty.

For teams running dozens of AI-assisted PRs per week, this division of labor—agents handle mechanical verification, humans verify intent and edge cases—keeps velocity high without sacrificing quality.

Building your own checklist

The lists above are starting points. Adapt them to your stack, your team's common bugs, and the specific AI tool you use. Track every bug that reaches staging or production: if three SQL injection vulnerabilities slip through in a month, add "parameterized queries" to the top of your checklist and make it non-negotiable.

Keep the checklist in a shared document or PR template so every reviewer uses the same standard. Update it quarterly as your AI tooling improves or new failure modes emerge.

Frequently asked questions

How long does a full checklist review take?

For a typical PR—200 lines, one or two files—plan five to ten minutes. Larger refactors or new features may need fifteen. The time investment is small compared to the cost of reverting a broken deploy or debugging a production incident.

Should I automate the checklist with linters and tests?

Yes, automate everything you can: formatters, type checkers, linters, security scanners, and unit tests. The manual checklist covers what automation misses—business logic, edge cases, and judgment calls. Tools like Goatfied's engineering GOATs can run automated checks before opening a PR, shrinking the manual workload.

Do checklists slow down AI-assisted development?

They shift time from debugging and fixing to upfront review, which is almost always faster. A bug caught at review takes minutes; the same bug in production takes hours. Teams that adopt checklists report fewer rollbacks and faster overall cycle time, even though each PR spends more time in review.

Can I use the same checklist for human-written and AI-generated code?

Yes, though AI code benefits more from the import, type, and error-handling items, while human code needs more scrutiny on complex business logic. Over time, your checklist will converge to cover both sources.

How do I measure whether the checklist is working?

Track two metrics: bugs found in review (should stay steady or rise as the checklist catches more) and bugs found in staging or production (should fall). If production bugs stay flat, your checklist isn't covering the right failure modes—update it based on incident post-mortems.

Related reading

• Goatfied vs Cursor vs GitHub Copilot: a benchmark across 50 real PR tasks