7 Spring Boot Mistakes AI Makes That Senior Developers Catch Immediately

A few weeks ago, I asked an AI assistant to build a Spring Boot REST API.

Within seconds, it generated controllers, services, repositories, DTOs — even unit tests.

I was impressed.

Then I looked closer.

The application compiled. The API worked. The tests passed.

But if I had deployed that code to production, I would have inherited several hidden problems that only become visible under real traffic, real users, and real maintenance.

That's when I realized something important:

AI is incredibly good at writing code. Senior developers are good at preventing future problems.

Here are seven mistakes I repeatedly see AI-generated Spring Boot code make — and why experienced developers catch them immediately.

1. Field Injection Instead of Constructor Injection

One of the most common patterns AI generates is this:

@Service
public class UserService {

    @Autowired
    private UserRepository repository;
}

@Service
public class UserService {

    @Autowired
    private UserRepository repository;
}

It works.

But it also creates unnecessary coupling, makes testing harder, and hides dependencies.

A senior developer usually prefers constructor injection.

@Service
public class UserService {

    private final UserRepository repository;
    public UserService(UserRepository repository) {
        this.repository = repository;
    }
}

@Service
public class UserService {

    private final UserRepository repository;
    public UserService(UserRepository repository) {
        this.repository = repository;
    }
}

Why?

Because dependencies become explicit, immutable, and much easier to mock during testing.

It's a small change that improves maintainability over the life of the project.

2. Missing Transaction Boundaries

AI often writes business logic like this:

public void placeOrder(OrderRequest request) {
    orderRepository.save(order);
    paymentRepository.save(payment);
    inventoryRepository.updateStock(...);
}

public void placeOrder(OrderRequest request) {
    orderRepository.save(order);
    paymentRepository.save(payment);
    inventoryRepository.updateStock(...);
}

Looks fine.

Until the payment succeeds…

…and inventory update fails.

Now your database contains inconsistent data.

A senior developer immediately asks:

"Where is the transaction?"

@Transactional
public void placeOrder(...) {
    ...
}

@Transactional
public void placeOrder(...) {
    ...
}

The goal isn't just making the code work.

It's making failures predictable.

3. Fetching Too Much Data

AI frequently writes repository methods like:

List<User> users = repository.findAll();

List<User> users = repository.findAll();

Seems harmless.

Until your table contains 15 million rows.

Or each user contains several lazy-loaded relationships.

Senior developers immediately ask questions like:

Do we need every column?
Can we paginate?
Should this be a projection?
Can the database do the filtering?

Efficient applications move only the data they actually need.

4. Weak Exception Handling

Many AI-generated examples look like this:

try {
    ...
} catch (Exception e) {
    e.printStackTrace();
}

try {
    ...
} catch (Exception e) {
    e.printStackTrace();
}

Or worse…

catch (Exception e) {
}

catch (Exception e) {
}

Production systems deserve better.

Experienced Spring Boot developers centralize exception handling.

@RestControllerAdvice
public class GlobalExceptionHandler {

    @ExceptionHandler(UserNotFoundException.class)
    public ResponseEntity<?> handle(...) {
        ...
    }
}

@RestControllerAdvice
public class GlobalExceptionHandler {

    @ExceptionHandler(UserNotFoundException.class)
    public ResponseEntity<?> handle(...) {
        ...
    }
}

Users receive meaningful responses.

Logs remain clean.

Developers can actually debug production issues.

5. No Validation at the API Layer

AI often accepts request bodies without validating them.

@PostMapping
public User create(@RequestBody UserRequest request) {

@PostMapping
public User create(@RequestBody UserRequest request) {

What happens if:

Email is empty?
Age is negative?
Name is 500 characters?

Production APIs should reject invalid requests before they reach business logic.

public class UserRequest {

    @NotBlank
    private String name;

    @Email
    private String email;

    @Min(18)
    private int age;
}

public class UserRequest {

    @NotBlank
    private String name;

    @Email
    private String email;

    @Min(18)
    private int age;
}

And don't forget:

@Valid
@RequestBody UserRequest request

@Valid
@RequestBody UserRequest request

Validation is much cheaper than cleaning corrupted data later.

6. Ignoring Performance Problems

AI usually optimizes for correctness.

Senior developers optimize for scale.

Imagine this code:

for(User user : users){
    orderRepository.findByUserId(user.getId());
}

for(User user : users){
    orderRepository.findByUserId(user.getId());
}

Everything works.

Until there are 50,000 users.

This creates the classic N+1 Query problem.

Experienced developers immediately look for:

JOIN FETCH
Entity Graphs
Batch fetching
Caching
Proper indexes

Performance problems rarely appear during development.

They appear in production — usually on Friday evening.

7. Logging Either Too Much or Too Little

AI-generated examples often use:

System.out.println(...)

System.out.println(...)

log.info("Everything...");

log.info("Everything...");

for nearly every line.

Neither is ideal.

Good logging answers questions like:

What failed?
Which user was affected?
Which request caused it?
How long did it take?

Useful logs look like this:

log.info("Created order {} for customer {}", orderId, customerId);

log.info("Created order {} for customer {}", orderId, customerId);

Logs should help future-you solve problems at 2 AM.

Not create new ones.

The Bigger Lesson

None of these mistakes are difficult.

Most are one-line fixes.

But collectively, they separate demo code from production code.

AI excels at generating code quickly.

Senior developers excel at asking better questions:

What happens if this fails?
Will this scale?
Is this secure?
Is this maintainable?
Can another developer understand it six months from now?

That's why AI isn't replacing experienced engineers.

It's changing what experienced engineers spend their time doing.

Instead of typing boilerplate, we spend more time reviewing architecture, improving reliability, and making smarter technical decisions.

And honestly…

That's the interesting part of software engineering.

Final Thoughts

AI is an incredible coding assistant.

I use it almost every day.

But I never assume generated code is production-ready.

The real skill isn't generating code anymore.

It's recognizing what the generated code forgot.

That ability is still one of the biggest differences between writing software that works today — and building systems that continue working years from now.

What Spring Boot mistake have you seen AI make most often?

Share your experience in the comments — I'd love to hear the patterns you've noticed.

Contents