AI-generated code does not always fail when you first run it. It may compile, pass the tests, and look fine during review. The problem sometimes shows up later when real users start using the feature or the code has to deal with a case that was not covered during development.
Six steps to fix AI-generated code
Hover or tap a step, or the test below the steps, for details.
This is a common problem for developers using AI coding tools. According to the 2025 Stack Overflow Developer Survey, 66% of developers said these tools often produce solutions that are almost right but not fully correct. Another 45% said debugging generated code takes more time.
When the code breaks, asking the tool to rewrite the whole thing is usually not a good place to start. First, reproduce the problem and write a test for it. Find out what is actually wrong. It could be a bad API call, missing error handling, incorrect logic, a security issue, or code that does not follow the rest of the application.
Fix that problem without changing more code than necessary. Rerun the test, check the rest of the application, and add a test or rule to catch the same mistake if it appears again.
This guide explains how to find and fix common AI-generated code errors, clean up code that has become difficult to maintain, and catch more of these problems before they reach production.
Why AI-Generated Code Creates Technical Debt That’s Hard to Detect
Problems in generated code are not always visible when a feature is first built. They often become clearer when the code needs to work with real data, support new changes, or deal with unexpected situations. The main risks usually show up in three areas: reliability, maintainability, and security.
Reliability
Generated code may work during early testing but start causing problems as you add more users and data. Slow database queries, missing error handling, failed requests, and uncontrolled background processes can cause timeouts, failed transactions, and higher infrastructure costs.
Finding the cause also becomes difficult when the application has poor logging or monitoring. The team knows something has gone wrong but has little information about where the problem started.
Our engineers check the parts of the application that users rely on most. We look for slow queries, unhandled errors, missing logs, and testing gaps that could cause problems after launch. We fix these issues and add the tests and monitoring needed to make the application more reliable.
Maintainability
Maintainability becomes a problem when a small change starts taking more work than it should. Generated code may repeat existing logic, add another way to do the same task, or follow a different structure than the rest of the application. As this builds up, developers have to spend more time understanding the code before making changes.
It also makes future development harder. The same logic may need to be updated in several places, and changing one feature could break another. When tests are missing, developers have even less confidence that a change is safe.
Our engineers find the parts of the code that are making development harder. We remove duplicate logic, clean up inconsistent patterns, and refactor code that has become difficult to change. We keep the parts that already work well, so you are not paying to rebuild an application that only needs focused improvements.
Security
Security problems are easy to miss when the main features appear to work. A user might be able to sign in and access their account normally while a missing permission check gives them access to another customer's data. Generated code may also leave credentials in the wrong place, accept unsafe input, or add dependencies with known security issues.
These problems become more serious once the application starts storing customer information, processing payments, or connecting with other business systems. A security issue at that point could expose private data or give someone access to parts of the application they should not be able to use.
Our engineers review authentication, user permissions, input handling, credentials, dependencies, and parts of the application that handle sensitive data. We fix the security gaps we find and add the checks needed before the code reaches production.
8 Common AI-Generated Code Errors and How to Fix Them
The reliability, maintenance, and security problems discussed above usually start with small code mistakes. Some are easy to find because they cause an error right away. Others stay hidden because the feature still works.
Knowing what to look for makes these issues easier to find and fix. Here are eight common problems developers should check for when reviewing generated code.
Eight problems to check for in generated code
Hover or tap a card to see how to fix it and how to catch it next time.
Incorrect API Calls and Missing Dependencies
Coding tools sometimes suggest methods or packages that do not match the version already used in the project. You might get a method that was removed in a newer version, an incorrect package name, or code that depends on a library that is not installed.
Adding the missing package straight away could create another problem. The project might already use a different library for the same task, or the new package might conflict with the existing setup. We check the current dependencies first and confirm what the project already uses before changing anything.
- How to spot it: Check for import errors, missing methods, build failures, and new packages that don't exist in the project.
- How to fix it: Match the code with the installed version and its official documentation. If the project already has a library for the same job, use that instead of adding another one.
- Catch it next time: Run builds, type checks, and dependency checks before merging the change. Review every new package before adding it to the project.
Outdated Libraries and Deprecated Syntax
Coding tools may generate code using an older library version or syntax that the project no longer uses. The code might still work today, but future updates could break it. An old dependency may also stop receiving bug fixes or security updates.
Updating everything to the latest version is not always the right fix. A major library update might change method names, configuration, or other parts of the application. We first check which version the project uses, whether it is still supported, and what other code depends on it. Then we update what is needed and test the affected features.
- How to spot it: Look for deprecation warnings, outdated packages, compiler warnings, and unmaintained dependencies.
- How to fix it: Move to a supported version and update the affected code. Test the related features before merging the change.
- Catch it next time: Track dependency updates and review them regularly. Run builds, tests, and dependency checks so outdated packages don't go unnoticed in the project.
Missing Edge Cases and Error Handling
Generated code often covers the expected flow but misses what happens when something goes wrong. A user might submit incomplete data, a database request could fail, or another service might take too long to respond. If we don't handle these cases, a small problem could stop the whole action or leave the user without a clear response.
We check failure paths alongside the normal user flow. If an operation depends on data being available, we check what happens when that data is missing. If it depends on another service, we check failed requests and timeouts. Errors should also be logged with enough information for the development team to find the cause without exposing technical details to users.
- How to spot it: Test missing values, invalid input, failed requests, timeouts, and other situations where the normal flow cannot continue.
- How to fix it: Add checks where failures are expected and decide what the application should do in each case. Return a useful message to the user and log the technical details needed to investigate the problem.
- Catch it next time: Add tests for failure cases when a feature is built, not after the first production error.
Before:
function getUserName(user: User | undefined) {
return user.name;
}After:
function getUserName(user: User | undefined) {
if (!user) {
throw new Error("User not found");
}
return user.name;
}TypeScript’s strictNullChecks helps catch cases where you use `null` or `undefined` without checking first.
Code That Misses the Actual Requirement
Generated code might work correctly but still not meet the business need. If the prompt omits an important rule, the tool has no reliable way to know it exists.
Think about a discount feature. The code might calculate a 10% discount correctly but apply it to every customer when the offer was only meant for first-time buyers. There is nothing technically wrong with the calculation. The mistake is in the business logic.
We check generated code against the actual requirement, expected user flow, and existing business rules. This is especially important for payments, pricing, permissions, subscriptions, and other features where a small logic mistake could affect customers or revenue.
- How to spot it: Compare what the feature does with the requirement and acceptance criteria. Test the business rules, not only whether the feature runs.
- How to fix it: Find the rule that was missed or misunderstood and correct that part of the logic. Avoid rewriting working code that is unrelated to the problem.
- Catch it next time: Give the coding tool clear requirements and acceptance criteria. Add tests for important business rules so the system catches changes that break them before release.
Duplicated Logic and Unnecessary Code
Generated code may add a new solution for something the application already does. You might end up with two functions that calculate the same value, different validation rules for the same data, or another package for a task the project already covers.
The problem becomes clearer when that logic needs to change. If the same rule exists in several places, a developer has to find and update every copy. Missing one could cause different parts of the application to behave differently.
We review the existing code before deciding what to keep. If the same logic already exists, we reuse it where it makes sense and remove code or dependencies that are no longer needed.
- How to spot it: Search for repeated calculations, conditions, validation rules, helper functions, and packages that do the same job.
- How to fix it: Keep one clear implementation where possible, and update the places that need it. Remove duplicate code and unnecessary dependencies after checking that nothing still relies on them.
- Catch it next time: Give the coding tool the relevant files and project rules before asking it to build a feature. Review generated changes against the existing code before merging them.
Code That Ignores Your Architecture and Conventions
Generated code may solve the task but place the logic in the wrong part of the application. A project might keep database work inside a service layer, while a generated feature connects to the database directly. Another change might use different naming, error handling, or folder structure from the rest of the codebase.
The feature still works, but developers now have two ways to work with the same application. As more changes follow different patterns, it takes longer to understand where code belongs and where a problem starts.
We check the surrounding code's structure before changing the generated code. We move misplaced logic into the right layer, follow the existing naming and error-handling rules, and keep the change consistent with the rest of the application.
- How to spot it: Look for database access in unexpected places, new folder structures, different error handling, inconsistent naming, or code that bypasses an existing service or shared component.
- How to fix it: Bring the change back into the project's existing structure. Keep the same layers, naming rules, shared components, and coding patterns where they still make sense.
- Catch it next time: Give the coding tool the relevant project files and architecture rules before asking it to make a change. Review generated code against the surrounding code before merging it.
Security Vulnerabilities in AI-Generated Code
A feature can work as expected and still have a security problem. The code might return the right data, process a payment, or update an account while missing a permission check or accepting input that should never reach the database.
These problems are easy to miss when testing only checks whether the feature works. We also need to check who can access the feature, what data they can send, where secrets are stored, and whether new dependencies are safe to use.
Our engineers review parts of the application that handle authentication, permissions, customer data, payments, credentials, and third-party packages. We fix the security issue and then test the failure case so the same problem doesn't slip through again.
- How to spot it: Check for missing permission checks, unsafe input handling, exposed secrets, weak authentication, and dependencies with known vulnerabilities.
- How to fix it: Correct the security issue directly and test both the normal flow and the case that should be blocked.
- Catch it next time: Run security and dependency checks before merging changes, and require a developer to review security-sensitive code.
Before:
query = f"SELECT * FROM users WHERE email = '{email}'"
cursor.execute(query)After:
cursor.execute(
"SELECT * FROM users WHERE email = ?",
(email,)
)The second version keeps the user input separate from the SQL command instead of inserting it directly into the query. OWASP recommends parameterized queries to prevent SQL injection.
Performance Bottlenecks and Inefficient Queries
Performance problems often stay hidden when an application has little data or few users. A page might load quickly with 20 records but slow down when the same code has to process thousands. Repeated database queries, unnecessary data loading, and repeated requests are common places to look.
We don't start by rewriting code that just looks slow. Our engineers measure where the application spends time and review the database queries behind the affected feature. Once we know what is causing the delay, we fix that part without changing unrelated code.
- How to spot it: Check slow pages and requests for repeated database queries, queries inside loops, large amounts of unnecessary data, or work that becomes slower as the dataset grows.
- How to fix it: Measure the slow operation first. Then reduce repeated queries, load only the data the feature needs, or change how related data is fetched.
- Catch it next time: Check database access when building features that work with lists or large datasets. Add performance tests around important flows as the application grows.
For example, this SQLAlchemy code can lead to an N+1 query problem. It loads users first, and accessing `user.orders` may then load orders separately for each user.
Before:
users = session.query(User).all()
for user in users:
print(user.orders)If the relationship uses lazy loading, the number of database queries grows with the number of users. SQLAlchemy's `selectinload()` loads the related records separately in batches instead.
After:
from sqlalchemy.orm import selectinload
users = (
session.query(User)
.options(selectinload(User.orders))
.all()
)
for user in users:
print(user.orders)Do not optimize a query only because it looks inefficient. Measure the slow part first, confirm what is causing it, and then make the smallest change that fixes the problem.
How to Fix AI-Generated Code: Step-by-Step
We have to understand what causes the issue before we can fix the generated code. Changing a large chunk of the app could fix one problem while causing another in the working code.
Our engineers start by tracing the reported issue back through the code. We find where it starts, fix it, and retest the application. The following six steps give you a simple process to follow.
1. Reproduce the Problem
First, we try to view the same problem ourselves. We repeat those steps and see where it fails, whether the customer can't make the payment, certain data on a page is missing, or a feature returns the wrong result.
Where possible, we write a test for the problem before making changes. It should fail before the fix and pass after. This provides a clear way to verify the change.
2. Trace the Code Behind It
It can be in a different location from where the error originated. The issue may involve business logic, database queries, dependencies, or previous code.
We read the code around the affected feature and follow the data through it. We analyze what the feature consumes, what it does with the data, and what other parts of the application use it. Until we know where the failure comes from, we don't start changing functions.
3. Identify What Is Wrong
Then we get down to the specifics. It may be missing error handling, wrong code, security concerns, duplicated code, an outdated dependency, or a slow query.
The solution will depend on what we discover. A permission problem requires a different approach than a slow database query. We separate these problems from the code before making changes so the work stays focused on the problem at hand.
4. Fix Only What Needs to Change
We start with one function, one query, and one condition, if any, that causes the problem. If part of the feature fails, you don't have to rewrite the whole feature.
For smaller changes, coding tools remain useful. Even then, an engineer should give the final code a once-over, especially if the change affects payments, permissions, security, or key business rules. There, we look at the change and decide where the product should go next.
5. Test the Fix and Review the Changes
After the change, we re-run the test for the original problem. We then test the related parts of the application and run the wider test suite.
Separate review of code changes is required. We look for unnecessary edits, removed validations, dependency changes, or anything else that seems to have crept in with the fix. Sometimes generated changes affect more code than expected, so we review the full diff before merging.
6. Stop the Same Problem from Coming Back
Addressing the existing error is just part of the solution. We retain the test for that bug in case a change brings back the same bug without anyone knowing.
Other issues require other checks. The credential may require a security check. The repeated-type problem might be better caught with stronger type checking. We add a test for a business rule when it is missed.
The check depends on the problem we found. It becomes part of the development process, so the team doesn't have to revisit the same issue later.
How to Refactor an AI-Generated Codebase Without Breaking It
AI-generated code may grow quickly as developers add features through separate prompts and changes. That is why one part of the code may follow a different pattern than another. You might find some logic repeated, a large function, or some important feature that has very few tests. Refactoring this code without checking how it already works could create new problems.
Our engineers review the code before making changes. We check the existing tests and add missing tests for the parts we plan to refactor. We then test each part individually and check each time we make a change.
Four steps to refactor without breaking working code
Hover or tap a step or an option for details.
Prioritize High-Risk Code Before Refactoring
Not all generated code requires refactoring. We first check the parts most important to the application and its users.
These include authentication, permissions, payments, and customer information, which, if they fail, could impact customers or key business operations. We also review code that fails frequently, changes often, or has insufficient tests.
Before changing these areas, we check how they work now and add tests for important flows where needed. A recurring error should be addressed before wasting time on an old file that may be "messy" but is still functional.
Add Characterization Tests Before Changing Existing Behavior
Tests are often missing from parts of a codebase built with AI coding tools. Before we refactor an area like this, we need to know what the existing code is already doing.
We record that behavior with characterization tests. We test the current inputs, outputs, errors, and other actions that the rest of the application depends on. These tests do not rule on the correctness of the existing behavior. They provide a history of events prior to code change.
We start with the important user flows and the code we plan to refactor. We then repeat tests after every change. If a change is unexpected, we observe it. Otherwise, we know the refactoring has affected existing behavior and needs another inspection.
Refactor in Small, Reversible Steps
If multiple parts of the generated code change at once, new problems are harder to trace. When one update modifies a data structure, logic, renames a function, and adds dependencies, it's hard to know what to check if something breaks.
Our engineers break up the work into more manageable changes. We might clean up one function first, remove duplicate logic in the next change, and move misplaced database code after that. We test throughout the process to ensure the application produces the expected results.
We also maintain one change per work. This makes the code easier to review and helps the team return to it when a change causes an issue. Reviewers can see exactly what you changed without having to rewrite everything at once.
Know When to Refactor and When to Rewrite
Messy generated code is not always a reason to start over. When most of the application works, and only parts are problematic, we build on the working sections rather than overhaul the whole application. Common issues like repeated logic, large functions, poor naming, and inconsistent coding patterns can often be fixed without replacing existing functionality.
A rewrite is worth considering when important changes to the code are difficult or unsafe to make. The architecture might no longer meet the product's requirements, the technology might have matured and no longer suit future development, or a part of an application might be too hard to maintain.
Our engineers verify what is still of value before recommending a rewrite. We assess business logic, dependencies, tests, existing technology, and the work required to move existing data and features. We also assess how much product knowledge already exists in the code.
In a large application, we don't need to replace everything at once. If we rebuild a part of it, we can replace that part without stopping the rest of the application. This reduces the amount of working code that needs to change at once.
Tools That Help You Find and Fix AI Code Issues
AI-generated code still needs the same checks as developer-written code. Some problems are easy to miss during a normal code review, especially when a large amount of code has been generated or changed at once. Development tools help us find these issues before they reach production.
Our engineers use different tools depending on what we need to check. Static analysis helps find problems in the code itself, while security and dependency scanners look for security risks, exposed secrets, and unsafe packages. Tests check whether the application still behaves as expected.
These tools speed up the review, but they don't replace an engineer. A scanner will not know that a discount was applied to the wrong customer or that a permission rule does not match the product requirement. The table below shows what each tool checks and what still needs developer review.
| Tool type | What it catches | What it can miss |
|---|---|---|
| Static analysis and automated fixes | Code smells, common bugs, rule violations, selected security issues | Wrong business logic, missed requirements, and architecture problems |
| AI code review bots | Suspicious changes, possible bugs, risky patterns, and review gaps | Domain-specific rules and problems that only appear at runtime |
| Secret scanning | Exposed API keys, tokens, passwords, and other supported credentials | Unsupported secret formats and credentials exposed outside scanned locations |
| Dependency scanning | Known vulnerabilities in packages and dependency versions | Unknown vulnerabilities and unsafe application logic |
| AI coding assistants | Test failures, runtime errors, implementation bugs, and some refactoring work when given enough context | Wrong assumptions, regressions, missed edge cases, and rules that were never documented |
Static Analysis Tools That Suggest Code Fixes
Static analysis tools check code for known problems without running the full application. They are useful for finding security issues, coding errors, and other problems that are easy to miss when reviewing a large amount of generated code.
SonarQube and GitHub CodeQL are two examples. CodeQL scans the code for potential security problems and coding errors. For supported alerts, GitHub Copilot Autofix also suggests changes that could fix the issue.
We use these suggestions as a starting point and review the code before accepting a fix. A tool may find unsafe code, but it does not understand every rule behind the product. It will not know that a discount was given to the wrong customer or that a feature does not match the original requirement.
Static analysis is useful for problems that follow known patterns. Business logic and product behavior still need tests and developer review.
AI Code Review Tools for Catching Bugs Before Merge
These AI tools give developers another check before generated code is merged. They review changes and flag code that may need more attention, such as missing checks, security issues, or incorrect logic.
GitHub Copilot code review, for example, reviews code changes and leaves comments with suggested fixes. Teams can also give it repository instructions about coding standards, testing, architecture, and areas that need closer review.
We use this feedback to find issues that might have been missed during the first review. The suggestions still need to be checked against the actual feature. A tool might find a missing null check but not know that a pricing rule or permission works differently from what the business requires.
The developer reviewing the change still needs to understand what the feature should do and decide whether the suggested fix belongs in the code.
Security Scanners for Secrets and Vulnerable Dependencies
Generated code may add a new package or leave a secret in the code without the developer noticing it. Security scanners help find these problems before the code reaches production.
GitHub Secret Scanning checks repositories for supported passwords, tokens, keys, and other credentials. Push protection blocks supported secrets before they are pushed. It does not find every type of secret, so we still check where credentials are stored and how the application uses them.
Dependabot checks the packages used in the project and alerts developers when it finds a known vulnerability. It shows which version is affected and whether a fix is available. Dependency review shows which packages were added, removed, or updated before we merge a pull request.
When a scanner finds a problem, we review it before making changes. If a secret is exposed, we may need to revoke or rotate it. If a package has a known vulnerability, we check for available updates and test the affected parts of the application after upgrading. These tools help us find known security problems earlier, but developers still need to review the final fix.
How to Use AI Coding Assistants to Fix Their Own Code
AI coding assistants give better fixes when they have the exact problem in front of them. Instead of asking the tool to fix a whole feature, give it the failing test, error message, relevant code, and any project rules it needs to follow.
Keep the task small and clear. If one function is failing, start with that function. Tell the assistant what it can change and what must stay untouched.
For example:
> Fix this function so the failing test passes. Do not change other files or add new dependencies. Return only the required changes.
Some coding agents can run tests and read the errors themselves. This gives the tool direct feedback on whether its change worked. If the tool does not have access to the development environment, provide the test failure and error details yourself.
Do not keep asking the assistant for new fixes if the problem is not clear. Security, business rules, permissions, data, or application structure issues require a developer to understand the cause first. The coding assistant can then help with a smaller part of the change.
After the fix, run the affected test and then the wider test suite. Check the code changes before merging them. A passing test shows one problem is fixed, but you still need to make sure the tool didn't change anything that should have stayed the same.
When to Bring in Outside Engineers
Not every AI-generated code problem needs outside help. If your team understands the application, can find the cause, and has enough tests to make changes safely, it usually makes sense to fix the code internally.
Outside help becomes useful when the team is spending too much time trying to understand the code or the same problems keep coming back. It also makes sense when the work needs security, architecture, or other experience the current team lacks.
You might need another engineer to look at the code when:
- Nobody is sure how a critical part of the application works. The feature runs, but changing it feels risky because the team doesn't know what else depends on it. The team needs to understand that part before adding more code.
- A fix in one place breaks something somewhere else. This often points to code connected in ways the team didn't expect. Shared logic, dependencies, or missing tests could be part of the problem.
- Important features do not have enough tests. This becomes more risky around payments, authentication, permissions, customer data, and business rules. Without reliable tests, it is hard to know whether a change has affected existing behavior.
- The team found a security issue but lacks the experience to investigate it properly. Exposed secrets, permission problems, unsafe input, or vulnerable dependencies should not be covered with another quick patch. Someone needs to find where the problem starts and what else it affects.
- The codebase has become difficult to follow. One feature might access the database one way while another follows a completely different pattern. The same can happen with authentication, error handling, and business logic, especially when different prompts have added generated code.
- It is unclear whether the code should be fixed or replaced. A rewrite might look easier when the code has become messy, but some parts of the application could still be worth keeping. A technical review helps separate the code that needs replacing from the code that only needs cleanup.
- Production problems keep coming back after they are fixed. At that point, fixing the latest error is not enough. The problem could lie deeper in the code, tests, dependencies, or how different parts of the application work together.
Outside engineers do not have to take over the project. At CodingCrafts, we can work with the existing development team to understand the codebase's difficult parts and identify what needs attention first. From there, we can fix the problem, refactor the affected code, or replace parts that no longer make sense.
How CodingCrafts Helps Teams Fix AI-Generated Code
When AI-generated code becomes hard to debug or change, the first step is figuring out what is actually wrong. At CodingCrafts, we review the code, tests, dependencies, and application structure before deciding what needs to change.
We deal with the urgent problems first. We address production bugs or security issues before duplicated code or poor naming. We also check the tests around important features, especially where a change could affect payments, permissions, customer data, or core business rules.
From there, the work depends on what we find. Some code only needs a small fix. Other parts may need better tests, cleaner logic, faster database queries, or refactoring. If a part of the application is no longer worth keeping, we can replace that part without rebuilding everything around it.
CodingCrafts can also work alongside your existing development team. We document important changes and keep the code and tests clear, so your developers know what was changed and why. They can keep working on the application after we finish the repair or refactoring work.
Not sure how much of your code needs fixing? Book a free 30-minute call with CodingCrafts. We’ll review the problem with you and help you decide what to fix, refactor, or replace.
Not Sure How Much of Your Code Needs Fixing?
Book a free 30-minute call. CodingCrafts reviews the problem with you, helps you decide what to fix, refactor, or replace, and can work alongside your team on the repair.