Larastan

The Flaw in Probabilistic Coding When we ask AI models to write code, we are dealing with probabilistic engines. They predict the next most likely token based on training patterns, not strict architectural validity. Even when you pack your system prompts with strict markdown instructions, cheaper models like DeepSeek V4 Flash skim details. They focus solely on delivering a seemingly complete file. The result is code that looks flawless on paper but breaks instantly on execution. To build resilient systems, we must couple non-deterministic AI generation with deterministic verification tools. Prerequisites Before implementing this self-correcting workflow, you should have: * A working knowledge of the Laravel framework * Basic familiarity with Filament admin panels * PHP and Composer installed locally Key Libraries & Tools * **Larastan**: A static analysis tool wrapper for PHPStan tailored for Laravel, which catches syntax, type, and namespace discrepancies. * **PHPUnit** / **Pest**: Testing frameworks used here to run automated smoke tests. * **DeepSeek V4 Flash**: A lightweight, cost-efficient LLM that executes tasks quickly but requires precise guardrails. Code Walkthrough When our cheap LLM generated our Filament resource, it completely hallucinated namespaces changed in the latest Filament updates. By running Larastan, we can instantly generate a precise bug list for the model to process. First, run static analysis to capture the exact syntax errors: ```bash ./vendor/bin/phpstan analyse ``` This command outputted 24 distinct errors, revealing that Filament table action classes were missing due to namespace changes. Next, we implement a basic automated smoke test. This test programmatically pings our Filament resource endpoints to ensure they return a successful status code instead of a fatal crash: ```php namespace Tests\Feature; use App\Models\User; use Tests\TestCase; class SmokeTest extends TestCase { public function test_filament_pages_are_accessible(): void { $user = User::factory()->create(); $this->actingAs($user) ->get('/admin/invoices') ->assertSuccessful(); } } ``` By feeding both the Larastan output and the failing test results back to the DeepSeek prompt, the agent gets a deterministic target. The model then successfully updates the code in under two minutes for pennies. Syntax Notes When working with Filament, watch your imports. Older tutorials and model training data often import `Filament\Tables\Actions\Action` directly, whereas newer versions utilize updated modular namespace paths. Static analysis highlights these missing classes immediately, saving you from manual browser debugging. Practical Examples This setup works perfectly in continuous integration (CI) pipelines. You can configure your Git agent to automatically spin up a cheap LLM instance to fix syntax anomalies caught by Larastan before a human reviewer ever opens the pull request. Tips & Gotchas Never rely on AI to perform framework upgrades, such as moving from Laravel 9 to 13. AI engines will make random syntax choices simply to make the test pass. For structural refactoring, stick to deterministic tools like Laravel Shift, which use strict rulesets like PHP Rector under the hood.