AI can produce working code in seconds. The real problem often appears later, when you need to change it.
A generated function may solve today's problem perfectly but become frustrating when another developer needs to understand it, extend it, or fix an unexpected edge case.
That happens because working code and maintainable code are not the same thing.
AI Optimizes for the Request, Not Your Future Changes
When you ask an AI coding assistant to solve a specific problem, it generally focuses on satisfying the request you gave it.
Suppose you ask:
Write a function that filters a list of products by price and category.
The generated function may work exactly as requested.
But what happens next month when the application also needs filtering by availability, rating, brand, and multiple categories?
If the original implementation wasn't designed with future changes in mind, adding those features can turn a simple function into a complicated collection of conditions.
This is one reason AI coding techniques should focus on more than simply getting code that runs.
Long Functions Are a Warning Sign
One common problem is generated code that tries to do too much in one place.
A function might validate input, transform data, communicate with an API, handle errors, format the response, and update the interface all at once.
It may still work.
But when something breaks, figuring out where the problem begins becomes much harder.
Smaller functions with clear responsibilities are often easier to understand and modify.
Comments Don't Automatically Make Code Maintainable
AI is very good at adding comments.
But comments can't rescue confusing architecture.
Consider code that requires ten comments to explain why several unrelated operations are happening inside one function.
A better solution may be to simplify the structure itself.
Comments are most useful when they explain why something unusual is necessary—not when they simply translate every line of code into English.
AI Can Introduce Unnecessary Abstractions
Another problem is overengineering.
Ask AI to build a small feature and you may receive interfaces, helper classes, configuration layers, utility functions, and several additional files.
Abstraction can be useful, but unnecessary abstraction increases the amount of code a developer has to understand.
A good question to ask is:
What complexity does this abstraction remove, and is that benefit worth the additional code?
If the answer isn't clear, the implementation may be more complicated than necessary.
Generated Code May Not Match Your Project
AI can generate perfectly valid code that still doesn't belong in your application.
For example, your project may already have a utility for handling API requests, but AI creates another one.
Your application may use one error-handling pattern, while the generated feature introduces a completely different approach.
Neither implementation is necessarily wrong.
The problem is inconsistency.
Over time, multiple approaches to solving the same problem make a codebase harder to understand.
Dependencies Can Create Long-Term Problems
AI may choose an external package because it provides a convenient solution.
That can save development time, but every dependency introduces another thing your project has to maintain.
Before accepting a generated dependency, ask:
- Do we actually need it?
- Could the feature be implemented using existing dependencies?
- Is the package actively maintained?
- Does it solve enough of the problem to justify adding it?
Sometimes a few lines of existing language features are preferable to introducing another library.
AI May Repeat Logic
Generated code can also duplicate similar logic instead of recognizing that several parts of the application need the same operation.
At first, duplication doesn't seem serious.
But eventually you may fix a bug in one location and forget to update the others.
When reviewing AI-generated code, look for blocks that appear to perform nearly the same job.
Ask AI to Follow Existing Patterns
One of the simplest ways to improve generated code is to provide the AI with relevant existing code.
Instead of:
Create a user authentication function.
try:
Here is the existing authentication service and two functions from this project that handle errors. Create the new authentication function using the same architectural pattern, naming conventions, error-handling approach, and dependency choices.
Now the AI has something concrete to follow.
This is much more reliable than asking it to "write clean code."
Ask for the Smallest Reasonable Change
When modifying an existing application, avoid giving AI an unnecessarily broad task.
Instead of:
Improve this entire application.
give it a specific objective:
Add pagination to the existing product list. Preserve the current API structure, UI behavior, naming conventions, and error handling. Do not modify unrelated components.
This reduces the chance of unnecessary changes spreading through the project.
Have AI Explain What It Changed
When AI modifies existing code, ask it to summarize the changes.
Explain exactly what you changed, which files were affected, why each change was necessary, and whether any existing behavior could be affected.
This gives you a quick review checklist.
It can also reveal when the AI changed more than you expected.
Use Tests as a Safety Net
Maintainability isn't only about readability.
A codebase is easier to maintain when developers can change it without being afraid of silently breaking something else.
Tests help provide that safety net.
When AI adds a feature, consider asking it to create tests for the important expected behaviors and edge cases.
Then review those tests rather than assuming that passing tests automatically prove the implementation is correct.
This works especially well alongside ready-to-use coding prompts that can help with debugging and code review.
Readable Code Usually Beats Clever Code
AI sometimes produces code that is technically elegant but difficult for the people working on the project to understand.
A clever one-line solution isn't necessarily better than several straightforward lines if the latter are easier to read and modify.
For long-lived projects, readability is a feature.
A Useful Review Checklist
Before accepting AI-generated code, ask:
- Does it follow the project's existing patterns?
- Does each function have a clear responsibility?
- Is there unnecessary duplication?
- Did AI introduce dependencies that aren't necessary?
- Are the names easy to understand?
- Are important edge cases handled?
- Are errors handled consistently?
- Are there tests for important behavior?
- Did the change modify anything unrelated?
- Could another developer understand this code six months from now?
The Goal Isn't Perfect Code
AI-generated code doesn't need to be perfect on the first attempt.
What matters is having a review process that turns a fast generated solution into code that fits the project.
AI is excellent at producing a starting point. Developers still need to decide whether that starting point is simple enough, consistent enough, secure enough, and maintainable enough to keep.
And that distinction becomes increasingly important as AI takes on more of the initial coding work: the faster code becomes to generate, the more important good review becomes.
