Common Mistakes When Using AI to Write Production Code in Laravel

· 5 min read
#ai #production #laravel #php #engineering

Introduction

AI-generated code often has one dangerous property: it looks finished. It comes with namespaces, classes, methods, and sometimes even tests. That surface-level completeness makes it easy to skip the deeper evaluation that production code requires.

For production systems, "it runs" is only the minimum bar.

Table of Contents

  • Why AI-generated code feels convincing
  • Authorization, validation, and domain context
  • Query cost, failure paths, and idempotency
  • Demo code vs production-oriented code
  • A more practical AI workflow
  • Merge checklist and FAQ

1. Trusting the First Draft Too Early

AI usually returns the most statistically common pattern, not the best pattern for your system. It may place business logic in observers or controllers simply because those patterns are common in public examples.

The fix is simple:

  • define the abstraction boundary first
  • only let AI generate code inside that boundary

Why AI-Generated Code Feels So Convincing

This is the danger: AI-generated code often looks complete.

  • class names are neat
  • formatting is clean
  • type hints are present
  • sample tests exist
  • comments may even look thoughtful

But production quality does not live in appearances. It lives in behavior under failure, system constraints, rollback safety, and maintainability over time.

2. Weak Authorization and Validation

AI often optimizes for the happy path. It may validate input fields but still miss authorization or invalid state transitions.

public function update(UpdatePostRequest $request, Post $post): JsonResponse
{
    $this->authorize('update', $post);

    $post->update($request->validated());

    return response()->json($post->refresh());
}

That example is small, but it highlights the right baseline: enforce authorization and use validated input.

3. Missing Domain Context

AI does not know whether a post can be republished, whether an order can be partially refunded, or whether a user may update their own role. Those are product rules, and they are often invisible in the short code slice used for prompting.

That is why syntactically correct Laravel code can still be logically wrong.

4. Queries That Work but Cost Too Much

An Eloquent query can be functionally correct while still being expensive in production because of:

  • N+1 queries
  • unnecessary select *
  • missing indexes
  • incorrect eager loading

AI sees code. It does not see your real data volume, cardinality, or query plan.

5. Missing Failure Paths and Idempotency

Production code must behave well when:

  • requests are retried
  • jobs run twice
  • providers time out
  • caches return stale data
  • external APIs respond in unexpected ways

AI rarely covers those paths unless you force it to.

6. Thin Tests

AI is good at generating happy-path tests. Production bugs rarely live there.

Always ask what happens when:

  • the job runs twice
  • the provider times out
  • the request is retried
  • stale cache is returned

7. No Operational Thinking

Code that talks to queues, webhooks, caches, or AI providers without timeout policies, retries, or logging is demo code, not production code.

Demo Code vs Production-Oriented Code

Demo version:

public function __invoke(Request $request): JsonResponse
{
    $summary = OpenAI::responses()->create([
        'model' => 'gpt-4.1-mini',
        'input' => 'Summarize: ' . $request->string('content'),
    ])->outputText;

    return response()->json(['summary' => $summary]);
}

A production-oriented version would also think about:

  • validation
  • timeout and exception handling
  • cache keys and prompt versioning
  • token usage logging
  • test fakes
  • response normalization

That code is slightly longer, but much more reliable.

A More Practical AI Workflow

Instead of prompting write feature X, break the process into:

  1. propose design and class boundaries
  2. write validation
  3. write the action class
  4. list failure modes
  5. write tests for those failure modes

AI usually performs much better when it is forced through these smaller steps.

Questions AI Should Answer Before It Writes Code

  • where are the side effects?
  • should anything be queued?
  • is the behavior idempotent?
  • which failure paths need tests?
  • where are the query risks?
  • does cache invalidation matter here?

If those questions remain unanswered, implementation is too early.

A Merge Checklist for AI-Assisted Production Code

  • Is authorization present where needed?
  • Is the input validated completely?
  • Are failure paths tested?
  • Are side effects moved out of the request cycle where appropriate?
  • Is there minimal logging or monitoring?
  • Can another reviewer understand the flow quickly?

FAQ

Should AI be banned for production code?

No. The problem is not AI itself. The real problem is using it without a review workflow that accounts for failure modes and trade-offs.

Is AI-generated code always worse than handwritten code?

No. For repetitive scaffolding and standard patterns, it can be very effective. The closer you get to core business logic and production risk, the more human judgment must dominate.

Key takeaways:

  1. AI-generated code often looks complete long before it is safe for production.
  2. The biggest risks usually sit in domain rules, authorization, query cost, and failure handling.
  3. Ask AI about side effects, idempotency, and test gaps before asking for implementation.
  4. Production-oriented code needs timeouts, logging, retries, normalization, and stronger tests.
  5. Human reviewers still own the final call where production risk is high.

Conclusion

AI is excellent at shortening the path from idea to first draft. Production code still requires three more layers of scrutiny: correctness, operability, and maintainability. If those are missing, early speed simply becomes delayed cost.

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