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February 14, 2026 9 min read

AI-Assisted Development: My Real-World Laravel Workflow

After a year of integrating LLMs into my daily engineering practice, here's what actually works — and where AI still gets in the way of real software craftsmanship.

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I’ve been writing software professionally since 2013. Over the past year, I’ve done something I never expected to do: I’ve let AI into my codebase, deliberately, strategically — and it has genuinely changed how I work.

But not in the way the hype cycle suggests.

The honest picture

AI coding tools don’t replace engineering judgment. What they do — when used correctly — is compress the gap between thinking and typing. That matters enormously when you’re shipping a product, managing a deadline, or just trying to get a proof-of-concept in front of a stakeholder by end-of-day.

My stack: Laravel 12, Vue 3, Flutter 3.x. Here’s where AI earns its keep.

Where it actually helps

Boilerplate and scaffolding. Laravel’s expressive API means there’s still a lot of ceremony around setting things up: migrations, factories, form requests, policies. I now dictate the shape of a resource to GitHub Copilot in a comment and let it draft the migration and factory. I review, adjust, done. Time saved: real.

RAG pipelines. I recently integrated a semantic search feature into a client project using Pinecone and the OpenAI embeddings API. Building the ingestion pipeline — chunking documents, generating embeddings, upserting vectors — is mostly plumbing code. AI wrote 80% of it; I audited the logic, added proper error handling, and wrapped it in Laravel jobs with retry logic.

Test generation. Writing Pest feature tests is one of those tasks that’s repetitive enough to be painful but important enough to never skip. I describe the happy path and the edge cases in a comment; Copilot drafts the assertions. My job becomes reviewer, not typist.

Where it falls apart

AI is confidently wrong about:

  • Application architecture. It will happily suggest putting business logic in controllers. Always. Every time. You have to fight it.
  • Security-sensitive code. Never accept AI-generated auth logic, raw SQL construction, or anything touching user permissions without a thorough audit. The model has no stake in your production system.
  • Domain-specific requirements. When a client has unusual business rules, AI has no context. It’ll generate plausible-looking code that fundamentally misunderstands the domain.

The workflow I’ve settled on

  1. Design first, generate second. Sketch the interface (class names, method signatures, return types) before touching Copilot. The model completes your intentions; it doesn’t replace them.
  2. Generate in small units. Ask for one method, not an entire service class. Smaller units are easier to review and less likely to contain subtle logic errors.
  3. Always read the diff. Every single line. No exceptions.
  4. Delete aggressively. AI tends toward verbosity. Every generated block of code is a starting point, not a final draft.

The deeper question

The developers who will thrive in this environment are the ones who understand why code is structured the way it is — not just how to produce it. AI raises the floor (less grinding boilerplate) while keeping the ceiling exactly where it always was (good architecture, good judgement, good taste).

That’s not a threat. It’s genuinely exciting.

Use the time you save on scaffolding to think harder about the problems that actually matter.