Cursor Best Practices for Production Code

Cursor can make you ten times faster. It can also create ten times more problems. The difference comes down to how you use it.

We’ve watched teams adopt Cursor and ship features at a pace they never thought possible. We’ve also watched teams adopt Cursor and spend the next three months untangling the mess it made. Same tool. Wildly different outcomes.

The difference isn’t skill. It’s discipline.

This post covers the eight rules we follow when using Cursor on production codebases. Not toy projects. Not hackathon demos. Real products with real users and real consequences when something breaks.

These rules come from shipping production code with Cursor every day. They’re opinionated. They work.

Cursor is powerful. That’s the problem

Let’s be honest. Cursor is genuinely impressive. The tab completion feels like it reads your mind. Cmd+K rewrites entire blocks with surprising accuracy. The chat mode can reason about your codebase in ways that feel like pairing with a senior engineer.

But that’s exactly the problem.

When a tool feels magical, you stop questioning its output. You accept the suggestion. You hit tab. You move on. And each unchecked suggestion is a small bet. Sometimes you win. Sometimes you introduce a bug that takes three days to find.

Power without discipline is how you end up with a codebase nobody can maintain. A codebase full of code that works but nobody understands. A codebase where every file has a slightly different pattern because Cursor improvised each time.

We use Cursor. Our engineers use it daily. We’re not anti-AI. We’re anti-sloppy.

These rules come from real projects. From watching what goes wrong and figuring out what prevents it. They’re not theoretical. They’re battle-tested.

The goal isn’t to slow you down. It’s to keep you fast without creating debt you’ll pay for later.

Eight rules for production Cursor usage

1. Create a .cursorrules file

This is the single most important thing you can do. Before you write a line of code with Cursor, define the rules it should follow.

A .cursorrules file sits in your project root and tells Cursor about your architecture, conventions, and constraints. Think of it as onboarding documentation for your AI pair programmer.

Include your folder structure. Your naming conventions. Your preferred patterns. Which libraries you use and which you don’t. How you handle errors. How you structure API responses.

Without this file, Cursor guesses. And its guesses are based on the internet’s average codebase, not yours. With this file, Cursor generates code that actually fits your project.

Spend thirty minutes writing a good .cursorrules file. It’ll save you hundreds of hours of cleanup.

2. One file at a time

Cursor can edit multiple files in a single pass. It’s tempting. Resist it.

Multi-file edits are where things go sideways. Cursor changes a function signature in one file and updates three callers, but misses the fourth. It refactors a component and quietly changes the props interface in a way that breaks a parent component two levels up.

Work one file at a time. Review the diff. Make sure it’s right. Then move on.

This feels slower. It’s not. Debugging a multi-file Cursor edit that went wrong is what’s actually slow.

3. Chat mode to diagnose, not fix

Cursor’s chat is excellent at understanding code. Ask it why something is broken. Ask it what a function does. Ask it to explain a stack trace.

But be careful about letting it fix things directly from chat. Chat-generated fixes tend to be broad. They change more than necessary. They sometimes “fix” the symptom by restructuring code in ways that introduce new problems.

Use chat to understand the problem. Then make the fix yourself, or use Cmd+K with a precise, targeted instruction. The narrower your prompt, the better Cursor’s output.

Diagnosis is where AI shines. Surgery requires a human hand.

4. Use .cursorignore

Not every file in your project should be in Cursor’s context. Some files are sensitive. Some are complex in ways that Cursor handles poorly. Some are simply not relevant and just add noise.

Create a .cursorignore file and add:

• Environment files (.env, .env.local)
• Database migration files
• Authentication and security configurations
• Lock files (package-lock.json, pnpm-lock.yaml)
• Generated files and build output
• Infrastructure configs (Terraform, Kubernetes manifests)

This isn’t just about security, though that matters too. It’s about focus. The less noise in Cursor’s context window, the better its suggestions. Keep the signal high.

5. Write tests before asking Cursor to implement

This one changes everything.

Instead of asking Cursor to implement a feature and then checking if it works, write the tests first. Define what the function should accept, what it should return, and what edge cases it should handle.

Then ask Cursor to make the tests pass.

You’ve just turned an open-ended creative task into a constrained implementation task. Cursor is dramatically better at the second kind. It has a clear target. It can verify its own output. And you have a safety net that catches mistakes immediately.

This is test-driven development, and it pairs with AI coding tools better than any other workflow we’ve tried.

6. Review diffs, not results

When Cursor makes a change, don’t just run the app and check if it works. Read the diff.

“It works” is a low bar. Code can work and still be wrong. It can work and introduce a security vulnerability. It can work and duplicate logic that already exists elsewhere. It can work and use a pattern that contradicts your architecture.

Read every line Cursor changed. Ask yourself: would I have written this? Does this match our conventions? Is there anything here I don’t understand?

If you can’t explain what a line does, don’t ship it. Full stop.

This is the habit that separates teams who use AI tools well from teams who accumulate invisible debt. It takes discipline. It’s worth it.

7. Small commits after every change

When you’re working with Cursor, commit early and commit often. After every successful change, make a commit.

This creates rollback points. If Cursor’s next suggestion breaks something, you can revert cleanly instead of trying to manually undo changes you didn’t fully understand in the first place.

A clean git history is your safety net. Without it, you’re one bad suggestion away from a messy afternoon of git diff archaeology.

Keep commits small and descriptive. “Add input validation to signup form” is good. “Cursor changes” is not. Future you will thank present you.

8. Know when to code manually

There are parts of your codebase where AI assistance is a risk, not a benefit. Know what they are.

Code these yourself:

• Authentication and authorization. A subtle bug here means users see each other’s data. Cursor doesn’t understand your security model deeply enough.
• Payment processing. Stripe integration looks simple until edge cases around webhooks, idempotency, and refunds start compounding. Get this right manually.
• Data migrations. A wrong migration can corrupt production data. Write these carefully, test them against real data, and don’t let AI improvise.
• Security-sensitive logic. Input sanitization, CSRF protection, rate limiting. These need to be correct, not just functional.

Cursor is a force multiplier. But multiplying carelessness in critical paths creates catastrophic outcomes.

Use Cursor for the 80% of code that’s standard application logic. Handle the critical 20% yourself.

What happens when you skip the rules

We’ve inherited codebases from teams that went all-in on AI coding without guardrails. The patterns are predictable.

Fix loops. Cursor introduces a bug. The developer asks Cursor to fix the bug. Cursor introduces a different bug. The developer asks Cursor to fix that one. Three iterations later, the code is twice as long, riddled with defensive checks that mask the original problem, and nobody knows what it’s supposed to do anymore. We’ve seen files balloon from 50 lines to 300 lines through fix loops alone.

Dead code everywhere. Cursor generates a new version of a function but doesn’t remove the old one. Or it creates a helper that’s used once and then becomes orphaned when the caller changes. Over weeks, the codebase accumulates dead weight that confuses everyone, including Cursor itself in future sessions.

Duplicated logic. Without clear architecture rules, Cursor reinvents utilities that already exist. You end up with three different date formatting functions, two validation schemas for the same data, and API wrappers that mostly overlap but diverge in subtle, bug-inducing ways.

Security vulnerabilities. This is the serious one. We’ve reviewed Cursor-generated authentication code that stored passwords with weak hashing. We’ve seen API endpoints with no authorization checks because Cursor scaffolded the route but skipped the middleware. We’ve found SQL queries that looked parameterized but weren’t.

These aren’t edge cases. They’re the natural outcome of treating AI-generated code as trusted code. It’s not. It’s suggested code. Suggestions need review.

When to bring in help

There’s a tipping point. You’ll know you’ve hit it when:

Cursor output requires more debugging than writing from scratch would have. You’re spending more time in fix loops than shipping features. The team is spending more time reviewing AI-generated diffs than actually building product.

At that point, the tool is costing you velocity, not adding it.

This usually happens when the codebase reaches a certain level of complexity. Early on, when the project is small and patterns are simple, Cursor is almost always helpful. As the codebase grows, as edge cases multiply, as architectural decisions compound, the gap between what Cursor suggests and what’s actually correct widens.

If you’re a founder watching your engineering team struggle with this, it might be time to bring in experienced engineers who know how to use these tools effectively. Engineers who can set up the guardrails, establish the patterns, and build the foundation that makes AI-assisted development actually work.

That’s what our MVP development service is built for. We set up codebases the right way from day one, with the architecture, conventions, and tooling that let you move fast without creating debt.

Ship with confidence

Cursor is here to stay. AI coding tools are going to keep getting better. The teams that win won’t be the ones who avoid these tools. They’ll be the ones who use them with discipline.

Start with the eight rules. Adapt them to your team. Enforce them consistently.

And if you want help getting it right, talk to us. We help teams ship production code faster, with AI tools as a multiplier rather than a liability.

If you’re dealing with a project that’s already been damaged by unstructured AI coding, read our guide on fixing a Cursor-built app. For teams evaluating alternatives, we’ve also written about GitHub Copilot best practices and Windsurf best practices.

Using Cursor for production code? Variant Systems helps teams use AI coding tools effectively without sacrificing code quality.