Does this checklist mean my app is guaranteed to work?

No. It covers the launch blockers that most commonly trip up AI-built apps — secrets, env vars, deployment, and database setup. It helps you validate readiness before real users see the app; it is not a substitute for testing your core flows by hand.

How is this different from the full readiness guide?

This is the short, checklist-first version. For the deeper explanation of why each category matters and how to reason about edge cases, see our guide on whether your app is ready to launch.

Can MakerToLaunch check all of this automatically?

MakerToLaunch scans your connected GitHub repo for common patterns across security, environment variables, deployment config, and database setup, and reports them with plain-language fixes. Manual QA items still need a human clicking through the live app.

What if I don't have a database or a separate backend?

Skip the sections that don't apply. A static frontend-only app still needs the security, environment variable, and deployment checks — it just skips database readiness.

How long should a pre-launch check take?

Most makers get through the automated items in a few minutes with a scan, then spend fifteen to thirty minutes on manual QA. Budget more time if you find and need to fix critical issues first.

Guides

AI App Pre-Launch Checklist: What to Check Before You Go Live

You built an app with an AI coding tool and it runs on your laptop. Before you send the link to anyone else, it helps to walk through a short pre-launch checklist covering the handful of things that quietly break AI-generated apps in production: leaked secrets, missing environment variables, a frontend still pointed at localhost, or a database that only exists on your machine. None of this requires deep technical background — it just requires checking the right things in the right order.

Updated August 2026 · 10 min read

Written for AI makers using tools like Cursor, Bolt, Lovable, Replit, Claude, and ChatGPT.

Quick answer

A solid AI app pre-launch checklist covers six areas: no secrets or API keys committed to GitHub, debug mode off in production, every environment variable set correctly on your host, your frontend calling the production backend instead of localhost, a deploy that actually builds and starts, and a production database if your app stores data. After the automated items, spend fifteen minutes clicking through your live URL by hand. MakerToLaunch can scan your GitHub repo for common launch blockers in most of these categories in a few minutes — it helps you validate readiness, it does not replace testing the app yourself.

Who this is for

You used an AI coding tool to build an app and are about to deploy or just deployed it
You want a short, ordered checklist instead of a long theory-heavy guide
You are about to share a live URL with beta users, friends, or paying customers

What you'll need

Your project pushed to GitHub, or ready to push
Login access to your hosting dashboard (Vercel, Railway, Render, or similar)
About twenty to thirty minutes to work through the list, plus time to click through your live app

Why AI-built apps need a pre-launch check

An AI coding tool is very good at producing working code fast. It is less consistent at production concerns that only matter once strangers are involved: where secrets live, what happens when a variable is missing, whether the frontend still points at your local machine, and whether error pages leak internal details. These are not bugs in the traditional sense — the app runs fine for you — they are configuration gaps that only show up once the app leaves your laptop.

The pattern shows up across stacks and tools. A generated .env file with real API keys sits next to a .gitignore that forgot to list it. A frontend fetch call hardcodes http://localhost:8000 because that is what worked during development. A SQLite file holds all your test data, and nobody set up a production database. None of these are visible when you are the only user testing on your own machine.

A pre-launch checklist exists to catch this specific category of problem before real users see it: not "does every feature work perfectly," but "are the launch blockers that commonly trip up AI-generated apps handled." Treat it as a floor, not a finish line — you still get to keep improving after launch.

Security and API keys

Security issues are the ones that can cost you money or cause real harm within hours of launch, so they come first. The most common failure mode is straightforward: an API key or database password ends up committed to a public (or later-made-public) GitHub repository, and it gets scraped and abused before you notice.

Work through this list before your repo goes anywhere near a public host:

No .env, .env.local, or credentials files committed to GitHub — check with a safe-upload scan, not just by eye
No API keys, database passwords, or tokens hardcoded directly in source files
Debug mode disabled for production (NODE_ENV=production, DEBUG=False, or your framework's equivalent)
Admin routes, internal dashboards, or debug endpoints require authentication
If a secret was ever committed, it has been rotated — removing the file later is not enough

Environment variables

Environment variables are the bridge between the code sitting on GitHub and the app actually running on your host. When this category is wrong, the classic symptom is an app that works perfectly on your machine and throws errors — or a blank screen — the moment it is deployed.

An AI coding tool will often generate a .env file for local development but will not automatically carry those values over to your hosting dashboard, because it has no way to know your production values. That step is yours.

A .env.example file exists and lists every variable name your app needs, with placeholder values only
Every variable from .env.example is also set in your hosting provider's dashboard
Variable names match exactly, including capitalization — a typo here fails silently in most frameworks
Production values are production values, not test or sandbox keys left over from development
Frontend-exposed variables (things like NEXT_PUBLIC_ or VITE_ prefixed values) contain nothing sensitive, since anything with that prefix ships to the browser

Frontend/backend connection

If your app has a separate frontend and backend — a common pattern for AI-built apps — this is one of the most frequent launch blockers. The frontend code that called http://localhost:8000/api all through development needs to call your production backend URL instead, and that URL rarely updates itself.

Check the following before you consider the deploy finished:

The frontend's API base URL points at your deployed backend, not localhost or a staging URL
CORS settings on the backend allow requests from your production frontend origin
Authentication cookies or tokens work across the domains you are actually using in production
Both halves of the app are deployed — or you have deliberately merged them into a single host

Deployment configuration

Your host needs clear instructions for installing dependencies, building your app, and starting it. AI-generated projects sometimes have all the right code but nothing that documents how to actually run it in production, which leaves the host guessing.

Build command is defined and has been tested at least once (npm run build or equivalent)
Start command, or the output folder for a static/serverless deploy, is set correctly
Runtime version matches what your code expects (Node 20, Python 3.11, etc.)
All dependencies are listed in package.json, requirements.txt, or the equivalent manifest
Any hosting-specific config files are present when your platform expects them (vercel.json, Procfile, Dockerfile)
Your most recent deploy completed without critical errors in the host's build or runtime logs

Database readiness

If your app stores any data, production needs a database of its own — not the SQLite file that has been quietly living on your laptop. This is one of the most common first-deploy failures because it works flawlessly in local development and then disappears entirely (or errors out) once deployed.

A production database instance exists on your host (PostgreSQL, MySQL, or your chosen engine)
DATABASE_URL, or your framework's equivalent variable, is set correctly on the host
Migrations have been run against the production database, not just locally
Seed or test data has been reviewed — no leftover admin accounts with weak, guessable passwords

Watch out

User-facing manual QA

Everything above can be reviewed from your repository and config. It cannot confirm that your signup form actually creates a user, or that your checkout flow completes end to end — that still takes a human clicking through the app. A pre-launch scan checks common launch blockers; it is not a substitute for opening your live URL and trying it yourself.

Open the live URL in an incognito window, not just your regular browser with old sessions cached
Sign up, log in, and log out if your app has authentication
Complete the one core action your app exists for, start to finish
Submit an invalid form on purpose and confirm you see a friendly message, not a raw error page
Check the layout on a phone-sized screen
If you can, ask one person who did not build the app to try the same path

Common mistakes

Treating "it runs on my laptop" as proof it is ready
What happens: Real users hit missing environment variables, a frontend still calling localhost, or a blank screen.
How to fix it: Run a pre-launch check against your GitHub repo, then confirm production env vars and the live URL by hand before sharing widely.
Committing a real .env file early and forgetting about it
How to fix it: Add .env to .gitignore immediately, create a placeholder-only .env.example, and rotate any key that was ever pushed.
Leaving debug mode on because it was never turned off
How to fix it: Set your framework's production flag explicitly (DEBUG=False, NODE_ENV=production) as part of your deploy steps, not as an afterthought.
Skipping a production database and relying on local SQLite
How to fix it: Provision a hosted database, point DATABASE_URL at it, run migrations, and confirm data actually persists on the live URL.
Assuming an automated scan means the app is fully tested
How to fix it: Use a scan to review risky patterns in config and code, then still walk through signup, core features, and mobile by hand — a scan helps validate readiness, it does not replace using the app.

Copy-ready prompt

Copy-ready prompt for your AI coding tool

Paste this into Cursor, Claude, ChatGPT, Bolt, Lovable, or your AI coding tool.

Ready to paste

I'm about to launch an app I built with AI help. Review this repository for common pre-launch issues only — do not claim every feature works, just flag what you can verify from the code and config.

Check and report on:
1) Any secrets, API keys, or credentials that are committed or hardcoded
2) Debug or development settings that should be disabled in production
3) Whether .env.example exists and documents every required environment variable
4) Any hardcoded localhost URLs or frontend/backend connection issues
5) Build, start, or deploy configuration gaps for my stack
6) Database setup risks if the app stores data
7) Whether a README explains what the app does and how to run it

For each issue found: explain it in plain language, why it matters before real users see the app, which files are involved, and a minimal, safe fix. Do not invent problems you cannot point to in the repo.