Production Readiness Checklist for AI-Built Apps
I tested this myself. Here is the honest take.
What makes an AI-built app production-ready?
Production checklist: Walk through essential deployment and monitoring steps.
Production readiness means: all secrets in environment variables, rate limiting on public endpoints, error logging to an external service, health check endpoint, graceful shutdown handling, database connection pooling, automated tests passing, and security audit completed. The AI generates none of these automatically.
What security checks are mandatory?
Mandatory security checks: no hardcoded secrets, SQL injection scan, session management review (HttpOnly cookies, not localStorage), CORS configuration, input validation on all endpoints, rate limiting on auth routes. Run an automated security scanner before every production deploy.
How do you monitor AI-generated code in production?
Monitoring: set up error tracking (Sentry or similar), uptime monitoring, database query performance tracking, and API endpoint latency alerts. The AI does not generate any monitoring config. Add it manually. I use Sentry free tier ($0) and UptimeRobot free tier ($0).
Production checklist: env vars, rate limiting, error logging, health check, graceful shutdown, DB pooling, tests, security audit, monitoring, uptime alerts. AI generates none of these. Budget 2 hours to add them.
AI code in production without monitoring is like driving without insurance. It works until it does not. I had an AI-generated memory leak that crashed my app at 3 AM. No monitoring meant I did not know until users emailed me. Set up monitoring before you have users, not after.
I need to [task]. Give me the exact steps and common mistakes.
References
- Deployment guide
- Validate AI code quality
- AI code security
- Environment variables
- Vibe coding risks
How do you implement proper error handling in AI-generated code?
AI code rarely includes comprehensive error handling. Always add: try/catch blocks around external API calls, graceful degradation when services fail, user-friendly error messages (not raw stack traces), and logging that captures enough context for debugging. Specifically: wrap database queries in try/catch with specific error type handling, add timeout values to all HTTP requests (30s default), and implement retry logic with exponential backoff for transient failures. These are non-negotiable for production code.
What testing strategy works for AI-generated applications?
Start with unit tests for business logic (the parts AI generates correctly), integration tests for API endpoints, and E2E tests for critical user flows. AI handles unit test generation well — prompt it: “Write unit tests for this function covering happy path, edge cases, and error conditions.” For integration tests, focus on database queries and external API interactions. E2E tests should cover your revenue-critical paths (signup, checkout, payment). Aim for 70% coverage on critical paths; don’t chase 100% on boilerplate code.
How do you deploy AI-built apps safely to production?
Use a staged rollout: deploy to staging first, run automated tests, then promote to production. Never push directly from development to production. Implement feature flags so you can disable new functionality without redeploying. Set up health check endpoints that return 200 OK when the app is healthy. Configure automatic rollback: if error rates spike above 5% within 5 minutes of deployment, the system should automatically revert to the previous version. This safety net is essential when deploying AI-generated code you haven’t fully reviewed.
What monitoring and alerting should you set up for production?
At minimum: error tracking (Sentry for JavaScript, Rollbar for Python), uptime monitoring (UptimeRobot or Pingdom), performance monitoring (Core Web Vitals via PageSpeed Insights), and database query monitoring (slow query logs). Set alerts for: error rate spikes, response time degradation (p95 > 2s), disk space below 20%, and memory usage above 80%. For AI-built apps specifically, monitor token usage and API costs — AI-generated features can accidentally create expensive loops. Budget alerts at 50%, 75%, and 90% of monthly spend.
How do you ensure AI-generated code follows security best practices?
AI code often skips security fundamentals. Always add: input validation on all user inputs (never trust client-side validation alone), parameterized database queries to prevent SQL injection, CSRF protection on all forms, XSS prevention by escaping user-generated content, and rate limiting on public endpoints. Prompt the AI specifically: “Add input validation, sanitize all user inputs, use parameterized queries, and implement rate limiting.” Don’t assume the AI will include these — it typically won’t unless asked explicitly.
What deployment configurations are essential for AI-built apps?
Beyond the code itself, configure your deployment properly: set NODE_ENV=production or equivalent, enable compression (gzip/brotli), configure CORS headers explicitly, set Cache-Control headers for static assets, use HTTPS everywhere (no exceptions), and configure Content Security Policy headers. For hosting, choose platforms that handle SSL automatically (Vercel, Railway, Cloudflare Pages). Never deploy to a plain server without configuring a reverse proxy (nginx/caddy) and SSL certificates manually — this is the most common mistake in AI-built app deployments.
How do you handle database migrations in production?
Never run database migrations without a rollback plan. Before deploying, test migrations on a staging database with production-like data volume. Ensure migrations are idempotent (can be run multiple times safely). Always include both an “up” migration and a “down” migration for rollback capability. Deploy migrations before deploying the application code that depends on them. If a migration fails, roll back the migration first, then fix the issue. This prevents database schema mismatches that cause runtime errors in production. For AI-generated migrations, double-check that column types, constraints, and indexes match your actual requirements.
What is your incident response process for AI-built apps?
When something breaks in production, follow this process: 1) Acknowledge the issue within 5 minutes (even if you don’t have a fix yet). 2) Roll back to the previous stable version immediately — don’t debug in production. 3) Reproduce the issue locally using the same data and configuration. 4) Identify whether the bug is in AI-generated code, configuration, or infrastructure. 5) Fix and test locally before redeploying. 6) Document the incident and update monitoring to catch similar issues earlier. The key insight: speed of response matters more than finding the perfect fix. Get back to a stable state first, then investigate.
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