What to Do When Your AI-Generated App Is Almost Finished (And Stuck at 80%)

The 80% Trap in AI-Generated Applications

AI coding tools such as Cursor, Bolt, Lovable, Replit, and Claude Code have radically accelerated how quickly founders can prototype software. In a few days, an entrepreneur can generate a functional frontend, integrate mock charts, and stitch together third-party API calls.

However, as many teams discover, the remaining 20% often requires 80% of the effort. AI assistants lack persistent architectural context: they solve the prompt directly in front of them without understanding how subtle changes in one file impact database transactions, session cookies, or bundle size elsewhere.

Here is a practical engineering roadmap to turn an AI-generated prototype into a resilient, production-ready web application.

1. Audit Client vs. Server Boundaries

The most dangerous flaw we routinely discover in AI-generated prototypes is **secret leakage in client bundles**. Because generative models prioritize making the UI work immediately, they frequently place API secret keys (such as Stripe Secret Keys or database service role credentials) directly inside client React components (`'use client'`).

• **Move all third-party API mutations to Server Actions or Route Handlers.**
• Verify that only public keys (e.g. `NEXT_PUBLIC_STRIPE_PUBLISHABLE_KEY`) are exposed to the browser.
• Inspect the compiled JavaScript bundle using source map analyzers to confirm no private tokens leak into client memory.

2. Enforce Strict Database Isolation and Row-Level Security (RLS)

AI assistants frequently generate raw client-side queries against database platforms like Supabase or Firebase. If your table permissions are not secured with granular Row-Level Security policies, any authenticated user can inspect or delete records belonging to other tenants by manipulating the client request.

-- Example of proper RLS policy on tenant data

CREATE POLICY "Users can only read documents in their workspace" ON project_documents FOR SELECT USING ( workspace_id IN ( SELECT workspace_id FROM workspace_members WHERE user_id = auth.uid() ) ); ```

3. Consolidate Duplicated Component Trees

When you ask an AI tool to "add a modal here" and later "add a drawer over there," it rarely reuses existing primitives. Instead, it generates duplicate button styles, conflicting modal backdrops, and multiple overlapping state stores.

• Identify the core design tokens (typography, color surfaces, spacing).
• Consolidate redundant components into unified primitives (e.g., standardizing on shadcn/ui or custom Tailwind components).
• Centralize server-synced state using tools like TanStack Query or native Next.js server components instead of nested `useEffect` chains.

4. Stabilize Webhook Handlers and Idempotency

Prototypes frequently fail when handling asynchronous events like Stripe subscription renewals, external webhooks, or email verification callbacks.

1. **Verify cryptographic signatures** on every incoming webhook before processing payloads.
2. **Implement idempotency keys** to prevent duplicate database writes if an external service retries a webhook.
3. Return a `200 OK` quickly and offload heavy background calculations to asynchronous job queues.

5. Validate TypeScript and Clean Up the Build Pipeline

Before deploying to production infrastructure, run:

npm run build
npx tsc --noEmit
npx next lint

Resolve all `any` types, unhandled promises, and invalid prop definitions. A clean build pipeline with zero warnings ensures that future code changes won't cause catastrophic runtime crashes on production servers.

Summary

AI coding assistants are extraordinary accelerators for early exploration, but real users demand security, uptime, and data integrity. By dedicating time to forensic review, authentication hardening, and architectural cleanup, you transform an experimental prototype into a durable business asset.