7 Critical Checks to Make Sure Your Code Survives the Real World — Run It Now
You’ve built your AI app. The vibe coded demo sings, the UI sparkles, and your test cases—what few you’ve run—pass.
But here’s the truth no one tells you before launch: the internet is the ultimate stress test, and it will break your code in ways you never imagined.
Every bug in production is an open invitation for lost users, bad reviews, and sleepless nights.
That’s why I built this 7-point production-readiness scan — drawn from decades of battle scars, and specifically sharpened for AI-powered applications.
1. Code Structure That Doesn’t Collapse Under Pressure
When a critical bug surfaces at 2 a.m., will your code feel like a neat LEGO set… or a crumbling Jenga tower?
- Keep it modular and simple — every extra dependency is a future liability.
- Follow SOLID and Clean Code rules like your app’s life depends on it (because it does).
- Never mix logic and presentation — separate concerns like your sanity depends on it.
2. Architecture Built for Scale, Not Just for the Hackathon
An MVP can get away with duct tape. Your production app? Not so much.
- Frontend patterns like MVVM keep your logic clean.
- The client should never talk directly to the database — keep it safe behind a secure backend.
- Structure your layers (UI, logic, data) so you can replace one without breaking the others.
3. Error Handling That Anticipates Disaster
The first time your AI API times out, what will your users see? A cryptic error? A blank screen?
- Catch errors before they catch you — try/catch everything async.
- Log them with detail, not just “Something went wrong.”
- Handle every edge case you can think of — and then a few you can’t.
4. Security That Doesn’t Gamble with User Trust
AI builders often forget: your app is only as trustworthy as your weakest credential.
- Secrets live in
.env, not in your Git repo. Ever. - Store sensitive keys server-side only — “anon” in the client, “service_role” in the server.
- Never trust incoming data — validate, sanitize, lock it down.
5. Data That Behaves — and Stays Private
Bad data in = bad app out.
- Validate everything that crosses your backend border.
- Restrict file uploads to safe types, sizes, and scan them for nasties.
- If you handle sensitive info (payments, health, messages), encrypt it at rest and in transit.
6. Testing That Actually Protects You
Here’s the uncomfortable truth: if it’s not tested, it’s already broken — you just don’t know it yet.
- Unit tests for your core logic.
- Integration tests for the messy interactions.
- End-to-end tests for the real user flow.
- Run them automatically before every deployment — no exceptions.
7. Monitoring That Sees the Storm Before It Hits
Once you launch, bugs don’t disappear — they hide.
- Set up structured logging (e.g., Pino, Winston) from day one.
- Hook in Sentry or similar for crash reports.
- Track performance metrics so you know when latency spikes before Twitter tells you.
Why This Checklist Isn’t Enough on Its Own
Even with this list, you’re only scratching the surface. AI apps have unique pitfalls — prompt injection, model latency, cost spikes — that can’t be caught with generic audits.
That’s why I built the Comprehensive GPT Code Audit Prompt. It runs through your repository like a relentless senior engineer, scanning for:
- Architecture flaws
- Security leaks
- Edge case blind spots
- AI-specific vulnerabilities
- And dozens of silent killers that don’t show up until your app is in the wild
Run it before you launch. It’s the closest thing you’ll get to insurance for your AI app.
Get the GPT Code Audit Prompt and make sure your AI app survives the real world — and doesn’t take your reputation down with it.