Ship checklist: React app built with AI assistance (for design ops)

You've built a React app with AI assistance. The UI looks good, components respond, and the feature list is checked off. But before you ship to production, especially if design ops is counting on this tool, there are invisible gaps that will bite you later. This checklist walks you through the steps that separate a prototype from a shipping product.

TL;DR

• Performance & Security: Test bundle size, lazy-load routes, enforce HTTPS, sanitize user input, and validate all API requests before shipping.
• Monitoring & Ops: Set up error tracking, performance metrics, and logging so you catch problems users hit.
• Design Systems: Enforce component consistency with TypeScript, Storybook, and visual regression tests.
• Team Handoff: Document component APIs, deployment steps, and rollback procedures so design ops can maintain the app without you.

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Shipping a React app built with AI assistance is different from shipping a hand-crafted one. AI accelerates feature work, but it also introduces subtle bugs, inconsistent patterns, and gaps in the non-visible parts, error handling, loading states, accessibility, performance. The last mile isn't glamorous, but it's what keeps your app alive in production. This resource walks you through the practical steps to close those gaps before launch.

Performance: Start with the foundation

AI tends to import everything it needs upfront, which bloats your JavaScript bundle. Before you ship, audit what you're actually loading.

Bundle analysis is the first stop. Use npm run build to see your production bundle, then run it through webpack-bundle-analyzer or Vercel's built-in insights. Look for:

• Unused dependencies (crypto libraries, date pickers you don't use, old polyfills)
• Duplicated code across chunks (a sign components aren't being tree-shaken properly)
• Large third-party libraries that could be replaced with leaner alternatives

Code splitting and lazy loading come next. Every route that isn't the homepage should be lazy-loaded. Wrap your route components with React.lazy() and Suspense:

const Dashboard = React.lazy(() => import('./pages/Dashboard'));

<Suspense fallback={<LoadingSpinner />}>
  <Dashboard />
</Suspense>

This means users don't download the Dashboard code until they navigate to it. For a design ops tool with multiple views, this can cut your initial load by 40-60%.

Image optimization is where most AI apps waste bytes. Every PNG screenshot or SVG icon should be:

• Compressed (use TinyPNG or similar before importing)
• Served as WebP with PNG fallback
• Lazy-loaded if below the fold

If you're using Next.js, the Image component handles this automatically. If you're using plain React, use the loading="lazy" attribute on tags.

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Security: No shortcuts here

AI-generated code often skips authentication and authorization checks because the prompt didn't mention them. Before shipping, make sure:

API authentication is enforced. Every request to your backend should include a valid token (JWT, session cookie, API key, whatever your auth strategy uses). Don't assume the frontend is trusted. Design ops users will be using this from different networks, and you need to know who they are.

Input sanitization prevents XSS attacks. If users can type text into your React app, that text needs to be sanitized before rendering or before sending to your API. Use a library like DOMPurify to strip HTML tags:

import DOMPurify from 'dompurify';

const userInput = '<img src=x onerror="alert(1)">';
const safe = DOMPurify.sanitize(userInput);
// Output: (img tag stripped)

HTTPS only. Your production React app must be served over HTTPS, and your API must use HTTPS. AI code often gets deployed to HTTP by accident during prototyping. Set secure on cookies and disable HTTP entirely.

Environment variables for secrets. Never hardcode API keys, database URLs, or tokens in your React code. Use .env.local for development and set environment variables on your hosting platform (Vercel, AWS, Netlify, etc.) for production.

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Observability: You can't fix what you don't see

Production React apps fail silently. A user hits a button, nothing happens, and you don't know why. Observability, error tracking, logging, and performance metrics, is what separates a prototype from a shipping product.

Error tracking catches crashes before users report them. Set up Sentry, Rollbar, or LogRocket:

• Catches unhandled exceptions and logs them with full stack traces
• Captures breadcrumbs (user actions before the crash) so you can replay the issue
• Groups similar errors so you see the most impactful bugs first

Add it early in your app initialization:

import * as Sentry from "@sentry/react";

Sentry.init({
  dsn: process.env.REACT_APP_SENTRY_DSN,
  environment: process.env.NODE_ENV,
});

Performance monitoring tracks slow pages and API calls. Use Sentry's performance SDK or Vercel's Web Analytics to capture:

• Time to first contentful paint (FCP)
• Largest contentful paint (LCP)
• Cumulative layout shift (CLS)
• API response times

API logging tells you what went wrong on the backend. Log every API request (method, endpoint, status, response time, user ID) so you can trace a user's session if they report a problem. Don't log passwords or tokens.

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Design systems: Consistency at scale

AI generates components fast, but inconsistently. A button might have different padding in two places. An input field has a different focus state. These inconsistencies compound as the app grows.

TypeScript is non-negotiable. It catches prop errors at build time instead of runtime:

interface ButtonProps {
  variant: 'primary' | 'secondary';
  size: 'sm' | 'md' | 'lg';
  onClick: () => void;
}

const Button: React.FC<ButtonProps> = ({ variant, size, onClick }) => {
  // ...
};

Storybook documents every component in isolation. You generate interactive docs that design ops can use to verify consistency:

npm install -D @storybook/react
npx sb init

Write stories for each component variant (button primary, button secondary, etc.) so anyone can see exactly how components should look.

Visual regression testing catches unintended design changes. Tools like Chromatic (integrated with Storybook) take screenshots of every story and flag when pixels change.

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The shipping process

Staging environment mimics production exactly. Deploy there first, run the full checklist on staging, then promote to production. Catch 90% of issues this way.

Feature flags let you deploy code without releasing it. Wrap new features in flags so you can turn them on/off without redeploying:

if (featureFlags.newDashboard) {
  return <NewDashboard />;
}
return <LegacyDashboard />;

Monitoring the first 24 hours matters most. Watch error logs, performance metrics, and user behavior closely. Be ready to rollback if something's broken.

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An interactive checklist for your ship process:

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This interactive dashboard shows where typical React apps built with AI stand on key readiness dimensions. Most are strong on performance and security tooling but weak on observability and design consistency, exactly where small oversights cause production pain.

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FAQ

The difference between an app that works and an app that ships is attention to the invisible parts. Performance, security, observability, consistency, these aren't glamorous, but they're what separate products that last from products that fade. Build the checklist into your workflow, and you'll ship with confidence.