Using AI in Laravel Development: What Helps and What to Double-Check

AI has become part of everyday web development, and Laravel work is no exception. Used well, it removes a lot of repetitive effort. Used carelessly, it produces code that looks right and fails in production. This post covers where AI genuinely helps on a Laravel project, where it needs supervision, and how to add an AI-powered feature to an app without creating a security or cost problem.

Where AI saves real time

The best uses are the ones where the output is easy to verify:

• Boilerplate and scaffolding: form requests, API resources, factories, and migrations follow strict conventions, so an assistant gets them right most of the time.
• Test writing: describing a behaviour and asking for a Pest test is faster than typing the arrange-act-assert skeleton yourself. You still read every assertion.
• Explaining unfamiliar code: pasting a confusing query or a legacy controller and asking what it does is a quick way to get oriented.
• Refactoring with a safety net: extracting a class or renaming across files is low risk when the test suite is there to catch mistakes.

Where it needs supervision

AI assistants are confident even when they are wrong. These are the places I check most carefully:

• Version mismatches. Models are trained on older code. Laravel 12 has a streamlined structure, with middleware registered in bootstrap/app.php and no Http/Kernel.php. An assistant that does not know your version will happily suggest the old way. Check the official documentation for the version you run.
• Authorization. Generated controllers often forget policies or gates. Every action that touches user data needs an explicit authorization check, and a test that proves another user gets a 403.
• Database queries. Look for N+1 problems, missing eager loading, and queries inside loops. These pass a quick manual test and hurt later.
• Invented APIs. Sometimes a method or package option simply does not exist. If a call looks unfamiliar, look it up before you build on it.

A useful rule: treat AI output like a pull request from a fast junior developer. Read it, run it, and test it before it merges.

Adding an AI feature to a Laravel app

Beyond writing code, you may want to put AI inside the product, for example summarising text or answering questions on a support page. A few habits keep this safe.

Keep keys in configuration

Put the key in .env, expose it through a config file, and never call env() outside config:

// config/services.php
'ai' => [
    'key' => env('AI_API_KEY'),
    'model' => env('AI_MODEL'),
],

Call the API from a queued job

AI requests can take seconds. Do not make a visitor wait on one inside a web request. Dispatch a job and show the result when it is ready:

class SummariseEnquiry implements ShouldQueue
{
    use Queueable;

    public function __construct(public Enquiry $enquiry) {}

    public function handle(AiClient $client): void
    {
        $this->enquiry->update([
            'summary' => $client->summarise($this->enquiry->message),
        ]);
    }
}

Rate limit and validate input

An open AI endpoint is a bill waiting to happen. Rate limit it per user or per IP, cap input length with a Form Request, and log usage so you can see cost per feature.

Never trust the output

Model output is untrusted text. Escape it when rendering, do not execute it, and do not let it decide anything security-relevant. If a user can influence the prompt, assume they will try to change its instructions, so keep sensitive data and privileged actions out of reach of the model.

Mind privacy

Anything you send to a third-party API leaves your server. Do not send passwords, payment details, or personal data you have no right to share, and tell users when their text is processed by an AI service.

A sensible workflow

1. Write the requirement and the test first, or at least the expected behaviour.
2. Let the assistant draft the implementation.
3. Read the diff line by line, focusing on authorization, queries, and validation.
4. Run the tests and a code formatter such as Pint.
5. Commit in small steps so a bad suggestion is easy to revert.

Final thoughts

AI does not replace understanding Laravel. It rewards it. The more clearly you can describe what you want and the faster you can spot what is wrong, the more benefit you get. Keep the human responsible for design, security, and review, and let the assistant handle the typing.

If you are planning a Laravel project, or want an AI feature added to an existing app, you can get in touch and we can talk through what makes sense for your case.