AI can write a function in seconds, fix a syntax error, and even generate an entire small application from a description.
But code that runs successfully isn't automatically code that should be used in a real project.
AI-generated code can contain inefficient logic, hidden edge cases, unnecessary complexity, security problems, or assumptions that don't match the rest of your application.
The important skill isn't simply getting AI to write code. It's knowing how to evaluate what it gives you.
Working Code Can Still Be Wrong
Suppose you ask AI to create a function that calculates an average.
The function may work perfectly for normal numbers but fail when the input list is empty.
That means the code works under one condition but isn't necessarily correct for every valid input.
This is one of the biggest differences between "the code runs" and "the code is reliable."
Check the Assumptions First
AI often has to make assumptions when your instructions don't provide enough information.
For example, a prompt might ask:
Create a function that retrieves users from the database.
But the prompt doesn't specify the database system, schema, authentication requirements, expected response format, or error-handling behavior.
AI may choose reasonable defaults, but those defaults might not match your project.
Before using the code, identify which parts are based on assumptions.
Look for Edge Cases
AI-generated code often looks convincing when tested with the example included in the prompt.
The more important question is what happens when the input changes.
Check situations such as:
- Empty input
- Missing values
- Unexpected data types
- Very large input
- Duplicate values
- Invalid user input
- Network failures
The relevant edge cases depend on what the code actually does.
Security Should Never Be Assumed
One of the most important reasons to review AI-generated code is security.
A generated solution may technically work while handling user input unsafely, exposing sensitive information, using weak authentication logic, or constructing database queries in an unsafe way.
This is especially important when AI generates code that interacts with:
- Databases
- User accounts
- Payments
- File uploads
- APIs
- Private information
Don't treat generated code as secure simply because it looks professional.
Check Dependencies
AI may recommend a library because it is familiar or commonly used in examples.
That doesn't necessarily mean it's the best choice for your project.
Before adding a dependency, check whether you actually need it and whether it fits your existing stack.
Also verify the library's documentation and current status rather than relying entirely on AI's description.
Look for Unnecessary Complexity
AI sometimes produces more code than the problem requires.
A simple task might result in several helper functions, multiple abstractions, or a large dependency when a small piece of straightforward code would be easier to maintain.
More code means more places for bugs to hide.
Ask yourself:
Could another developer understand this code quickly without needing to decode several layers of abstraction?
If not, simplify it where appropriate.
Check Whether It Fits Your Existing Code
Generated code doesn't exist in isolation.
A function can be perfectly valid on its own and still be a poor fit for the application around it.
Check whether it follows your existing:
- Naming conventions
- Error-handling patterns
- Data structures
- Project architecture
- Dependency choices
- Formatting conventions
Consistency makes code easier to maintain.
Don't Ask AI to Rewrite Everything Immediately
When generated code has a problem, it can be tempting to paste everything back into AI and ask for a complete rewrite.
That can introduce new changes that weren't necessary.
A better approach is to identify the specific problem first.
The function works for normal input but fails when the array is empty. Identify the cause and make the smallest change necessary to handle that case. Preserve the existing behavior for valid non-empty input.
This gives AI a much narrower task.
Ask AI to Explain Its Own Code
One useful review technique is to ask AI to explain generated code line by line or section by section.
If you don't understand why a particular piece exists, that's a good reason to investigate it before using it.
You can also ask:
Identify any assumptions this code makes about the input, environment, dependencies, or application architecture.
This can reveal problems that aren't obvious from simply reading the code.
Test Before Trusting
Testing is more valuable than simply asking AI whether its code is correct.
Give the implementation realistic inputs and deliberately test unusual cases.
For important code, automated tests can provide repeatable verification and make future changes safer.
AI can help create those tests, but the tests themselves should also be reviewed.
Use AI as a Coding Assistant, Not the Final Authority
AI is extremely useful for generating ideas, explaining unfamiliar code, creating test cases, finding potential bugs, and speeding up repetitive development work.
But the developer still needs to decide whether the solution is appropriate.
A good workflow is:
- Describe the problem clearly.
- Generate a possible solution.
- Understand how the solution works.
- Test normal and unusual cases.
- Review security and dependencies.
- Check that it fits the existing project.
- Simplify unnecessary complexity.
The goal isn't to avoid AI-generated code.
It's to avoid treating generated code as automatically correct.
AI can dramatically reduce the time needed to write software, but human review remains important because software has to work within a real environment with real users, data, constraints, and failure conditions.
For more practical coding and AI techniques, explore the Coding section or browse ready-to-use prompts in the Prompt Library.
