Production readinessP0CursorClaude CodeLovableBolt.newReplit AI

AI-Generated Code Audit Before Production

Review AI-generated code before it reaches real users. Identify hidden risks in auth, database access, APIs, deployment, and maintainability.

codeauthdataarchitecture

Initial verdict

Short answer

high risk

Review AI-generated code before it reaches real users. Identify hidden risks in auth, database access, APIs, deployment, and maintainability.

Get a Fix-or-Rebuild Review Back to all build problems

Quick answer

AI-generated code can work in a demo and still carry production risk. A review should identify hidden assumptions, unsafe boundaries, duplicated logic, and maintainability problems before real users depend on the app.

Why this happens

Cursor, Claude Code, Lovable, Bolt, and Replit can generate working code quickly. The risk is that the code may satisfy the prompt without fully understanding your product rules, auth model, database ownership, deployment environment, or long-term change path.

What to check first

Duplicated logic across pages, API routes, components, and server functions.
Frontend-only checks for permissions or paid features.
Secrets, keys, or sensitive identifiers exposed to browser code.
Hardcoded demo data or test user assumptions.
Inconsistent permission logic between frontend, backend, and database policies.
Unsafe API calls that trust client-provided user or tenant IDs.
Missing error handling around auth, storage, payments, and database writes.
Fragile architecture that makes every new change risky.

What not to do

Do not ask AI for a whole-file rewrite before isolating the risk.
Do not ship code just because it compiles.
Do not hide data in the UI while leaving backend access open.
Do not move secrets to client code to make deployment pass.
Do not patch production code without knowing what layer is failing.

Safe next step

Review the AI-generated code by risk layer: auth, data access, APIs, deployment, and maintainability. The output should be a safe change sequence and a decision about whether to fix, migrate, rebuild, or launch.

Review My AI-Generated Code

FAQ

Is this a complete implementation audit?

No. It is a structured production risk review focused on likely failure layers and safe next steps.

Can AI fix the issues after review?

Sometimes. The review should define what AI can safely change and what it should not touch.

What if the code was generated by several tools?

That is common. The review should look for inconsistent assumptions between tools and prompts.

Should I rewrite everything?

Not automatically. First decide whether the app needs a fix, migration, rebuild, or launch path.

If this is not your failure layer

These are nearby failure patterns that may better match your situation.

Auth / database / permission problems

AI App Authentication Broken? Check the Boundary Before Regenerating Code

AI-generated auth failures often come from redirect loops, callback mismatches, session handling, client/server boundaries, or unclear user-role design. Identify the auth boundary before regenerating code.

Open diagnosis

Auth / database / permission problems

AI App Database or Permission Problem? The Issue May Be the Data Model

AI-generated database and permission failures often come from wrong schema, missing relations, unclear data ownership, or confused RLS and access rules. Identify the data-model failure layer first.

Open diagnosis

Deployment problems

AI App Deployment Failed? Local Success Does Not Mean Production Ready

AI-built apps often fail in deployment because of build errors, runtime mismatches, env vars, database connections, auth redirects, or serverless limits. Identify the deployment failure layer first.

Open diagnosis

AI-built app problems

AI-Built App Backend Not Working: API, Database, Auth, or Deployment?

If the backend of your AI-built app is failing, the issue may be deeper than one endpoint. Learn how to identify whether API, database, auth, or deployment is broken.

Open diagnosis

Decision review

Not sure whether to fix, rebuild, migrate, or stop?

If this problem involves auth, database access, payments, deployment, user data, or an AI-generated codebase that keeps breaking, another prompt may make the project harder to recover. A Fix-or-Rebuild Review identifies the broken layer and the safest next step before you spend more.

Use this when you need a decision before hiring again, prompting again, or launching.

Get a Fix-or-Rebuild Review

Related failure paths

Fix or rebuild decisionDecide whether the app is still worth patching.AI-built MVP not workingWhen an AI-built MVP breaks before launch.Works locally, fails onlineWhen local success fails in production.AI-generated code failingWhen the generated code keeps failing.