How to Review Cursor-Generated Code Before You Launch

Cursor makes it easy to produce a lot of working-looking code quickly. That speed is the point, and also the problem: you can end up owning a codebase that nobody, including you, has read line by line. Before real users arrive, someone has to. This guide shows how to review AI-generated code so your Cursor app is genuinely production ready, and where to start if you want a quick read on your current position with the free AI App Health Check.

Why Cursor code needs its own review

Cursor is an AI-assisted code editor. It does not host your app, so unlike a hosted builder there is no platform deciding how your database, auth or deployment work. Your code sits in a repository you deploy yourself, which gives you full control and full responsibility. Nothing is hidden, but nothing is checked for you either.

AI-generated code tends to fail in recognisable ways. It is locally plausible but globally inconsistent: each file looks fine, while the same rule is implemented three different ways across the project. Cross-cutting concerns such as authorization, validation and error handling are the first things to go missing, because no single prompt asked for them. For the security angle specifically, see our post on vibe coding security risks before launch. This article is about the code itself.

Step 1: Automate the cheap checks first

Do not spend human attention on things a machine can check in seconds. Set these up once and run them on every change.

Type checks and linting

Turn on strict type checking (for example TypeScript strict mode) and a linter. AI code often leans on loose types, unused variables and silently ignored errors. A strict compiler turns many of those into visible failures. Fix the errors rather than silencing them with ignore comments, and count how many suppressions already exist: a high number is a signal in itself.

Dependency and licence checks

Run your package manager's audit command and review the output. Then read the dependency list with a sceptical eye: packages the model suggested may be abandoned, mistyped lookalikes or simply unnecessary. Check that each one exists, is maintained and has a licence compatible with how you plan to sell the product. Remove what you do not use.

Secret scanning, including history

Scan the current files and the full git history for API keys, tokens and connection strings. A key deleted in a later commit is still in history, so rotate anything that was ever committed rather than just removing it. Add a pre-commit or CI secret scan so it does not happen again.

A CI pipeline that gates merges

Put the checks above, plus your tests and a production build, into continuous integration, and block merging when they fail. This is the single most useful guardrail for AI-assisted work, because it makes the standard independent of whichever prompt produced the change.

Step 2: What needs a human reviewer

Automation catches mechanical problems. These need judgement.

Architecture and boundaries

Draw the map: where does data enter, where is it stored, where do third-party services connect? Look for business logic buried in UI components, database calls scattered across files, and server-only code that could end up in the browser bundle. If you cannot describe the structure in a paragraph, new features will keep colliding. Our overview of software architecture explains the vocabulary.

Dead and duplicated code

Iterating with an AI assistant leaves debris: abandoned files, three versions of the same helper, and old routes that still respond. Dead code is not harmless. Forgotten endpoints are still reachable, and duplicates drift apart so that a fix in one copy never reaches the others. Delete what is unused and consolidate what is duplicated before adding anything new.

Authorization on every route

List every route, API handler and server action, and for each one write down who may call it and how the code enforces that. Logging in is not the same as being allowed. The classic failure is a handler that accepts a record ID and returns it without checking the record belongs to the caller. If your data layer uses row-level rules, read common Supabase RLS mistakes in AI-built apps.

Input validation and injection

Anything that arrives from outside, such as form fields, query strings, uploaded files, webhooks and model output, is untrusted. Check that it is validated on the server, not only in the browser, and that database queries are parameterised rather than built by string concatenation. If your app passes user text to an LLM or renders model output as HTML, treat that as untrusted input too.

Error handling and logging

Look for empty catch blocks, errors swallowed so the screen looks fine, and responses that leak stack traces or internal messages. Production needs errors that are caught, shown to users in plain words, and recorded somewhere you will actually look. Without that, the first sign of a problem is a customer email.

Tests that actually test behaviour

AI tools write tests eagerly, and not all of them are worth much. Watch for tests that mock the very thing they claim to test, assertions that cannot fail, and snapshots accepted without reading. A quick check: break a rule on purpose, such as removing an authorization check, and see whether any test goes red. If none does, the tests are decoration. Write behavioural tests for sign-in, permissions, payments and anything that changes data.

Rules files and prompt drift

Many Cursor projects carry project rules or instruction files that steer the assistant. Read them. They accumulate contradictory instructions, outdated conventions and sometimes sensitive details that should not live in a repo. If the rules say one thing and the code does another, future generated code will follow the rules, so keep them short, current and consistent with how the project is really built.

A code review checklist for AI-generated code

Use this as a pass or fail list. Anything you cannot confirm counts as a fail.

• The project builds from a clean checkout with documented steps.
• Strict type checking and linting pass without blanket suppressions.
• Dependency audit reviewed; unused and unmaintained packages removed; licences checked.
• No secrets in current files or git history; leaked keys rotated.
• Every route and server action has a stated, enforced access rule.
• All external input validated on the server; queries parameterised.
• Errors are handled, logged and never expose internals to users.
• Tests cover permissions, payments and data changes, and fail when behaviour breaks.
• Dead code and duplicates removed; the structure can be explained simply.
• Rules files and prompts are current and contain no secrets.
• CI runs checks and the build, and a deployment can be rolled back.

For the wider launch picture, including accounts, payments and operations, pair this with our production readiness checklist.

A step-by-step rescue path

If the review turns up more than you can fix in a weekend, do it in this order so each step makes the next one safer.

1. Freeze features. Stop generating new code until the foundations are checked.
2. Snapshot and protect. Tag the current state, move the repo to a private location you control, and rotate any exposed secrets.
3. Turn on the automated gates. Types, linting, audit, secret scanning and CI, fixing failures as they surface.
4. Map and prioritise. List routes, data stores and integrations, then rank risks by what could harm users or money first.
5. Fix authorization and validation. These are the changes with the highest consequences.
6. Add behavioural tests around what you just fixed, so it stays fixed.
7. Clean up. Remove dead code and duplicates, and update the rules files.
8. Stage, then launch. Deploy to a staging environment, test the real flows, add monitoring, and keep a rollback ready.

Not sure whether you are at step 1 or step 8? Our post on when to stop prompting and hire an engineer helps you decide.

Where UnlockLive fits

You do not need to hand over your project to benefit from an expert read. Our AI App Technical Audit gives you an independent review of the architecture, security-relevant code paths, dependencies and tests, with a prioritised list of what to fix. If you would rather have it fixed, AI App Repair & Launch covers the repair, hardening and deployment work so the app can go live with confidence. Our engineers work from Toronto and our Dhaka engineering centre, and we are happy to work alongside Cursor rather than against it. If you are unsure what kind of review you need, see technical audit vs penetration test vs code review.

Your next step

Start with the cheap checks: turn on strict types, run the audit and scan your history for secrets. Then run the free AI App Health Check to see which areas need attention first. No tool can promise that code is flawless, but a measured, repeatable review will tell you far more than the fact that the app runs on your laptop.