The Config File That Fixed My AI Coding Assistant

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I’ve been frustrated with AI-generated code for months.

Not the big stuff — the AI can handle complex algorithms and architectural patterns reasonably well. It’s the small stuff that kills me. Missing error handling. Hardcoded values. Functions that are 200 lines long when they should be 50. The kind of code that works in development and explodes in production.

I’d spend 20 minutes per session editing AI output. Adding the try-catch blocks it forgot. Splitting the monolithic functions. Replacing the magic numbers with named constants. By the time I was done, I’d rewritten 40% of what the AI gave me.

Then I found a .cursorrules file on GitHub. Thirty lines of configuration. I pasted it into my project root and forgot about it.

The next day, the AI’s output was unrecognizable. In a good way.

What the File Does

It’s not magic. It’s not even clever. It’s just… specific.

The file tells the AI who it’s supposed to be and what standards it should follow. Think of it as a code review checklist that the AI reads before every response.

Here’s the core of it:

You are a senior software engineer. Follow these rules:

- Always include error handling with specific error types
- Never use `any` type in TypeScript
- Functions must be under 50 lines
- Use immutable patterns — never mutate objects directly
- Always validate user input at system boundaries
- Write tests for all public functions
- Use meaningful variable names
- No console.log in production code

That’s it. Eight rules. But the effect was dramatic.

Before and After

The same prompt — “add email validation to the registration form” — produced completely different results.

Before the config:

function validateEmail(email) {
  if (email.includes('@')) {
    return true
  }
  return false
}

Technically correct. Useless in production.

After the config:

function validateEmail(email) {
  if (!email || typeof email !== 'string') {
    throw new ValidationError('Email is required and must be a string', 'INVALID_EMAIL_TYPE')
  }

  const trimmed = email.trim().toLowerCase()

  if (trimmed.length > 254) {
    throw new ValidationError('Email exceeds maximum length', 'EMAIL_TOO_LONG')
  }

  const emailRegex = /^[^\s@]+@[^\s@]+\.[^\s@]+$/
  if (!emailRegex.test(trimmed)) {
    throw new ValidationError('Invalid email format', 'INVALID_EMAIL_FORMAT')
  }

  return { valid: true, normalized: trimmed }
}

Same task. Same AI. Radically different output.

Why This Works

AI models don’t have opinions about code quality. They have probabilities. When you ask for a function without context, they generate the most statistically common implementation — which is usually tutorial-level code.

When you add standards, you’re shifting the probability distribution. The model now generates code that matches the patterns described in the config. It’s not getting smarter. It’s getting more aligned.

The key insight: AI coding assistants don’t need better models. They need better instructions.

The Config I Actually Use

I’ve modified the original 30 lines over the past month. Here’s my current version, tailored for a TypeScript/Node.js project:

You are a senior software engineer writing production TypeScript.

Code standards:
- Functions under 50 lines, files under 400 lines
- Always handle errors with specific error classes
- Validate all input at system boundaries
- Use immutable patterns (const, spread, no mutation)
- Never use `any` — use `unknown` and narrow with type guards
- Prefer composition over inheritance
- Use async/await, not raw promises

Testing:
- Write unit tests for all public functions
- Include edge cases: empty input, null, boundary values
- Test names should describe the scenario, not the method

Security:
- No hardcoded secrets, URLs, or credentials
- Parameterized queries for all database operations
- Sanitize user input before processing
- Log security-relevant events

Style:
- Descriptive variable names (no single letters except loops)
- Comments explain why, not what
- Follow existing project conventions
- No console.log — use structured logging

Sixty-two lines. Takes 10 seconds to paste into a project. Saves hours of cleanup.

The Limitations

The config doesn’t fix everything. Two things it can’t help with:

First, context-dependent decisions. The config says “functions under 50 lines,” but sometimes a function needs to be 80 lines to be readable. The AI doesn’t know when to break its own rules. You still need judgment.

Second, domain-specific logic. The config handles general code quality, but it can’t teach the AI about your business rules. If your payment system has special handling for enterprise customers, the config won’t capture that. You need documentation or explicit prompts for that.

What I’ve Noticed Since Using It

Three things changed beyond just code quality:

My review time dropped. I used to spend 40% of my coding time reviewing and fixing AI output. Now it’s about 15%. The config didn’t eliminate review — nothing should — but it eliminated the tedious parts.

My prompts got shorter. Before the config, I’d write detailed prompts explaining my coding standards. Now I just describe the task. The config handles the standards.

My junior developer’s code improved. I shared the config with the junior developer on my team. Not for the AI — for them. It’s a concise summary of our coding standards. Their code quality improved within a week.

How to Write Your Own

Don’t copy mine. Write your own based on the code you actually want to see.

Start with the problems that frustrate you most about AI-generated code. For me, it was error handling and function length. For you, it might be naming conventions or test coverage.

Then write rules that address those specific problems. Be concrete. “Write good code” is useless. “Functions under 50 lines with specific error types” is useful.

Keep it under 100 lines. Longer configs get ignored — by the AI and by you.

Update it as you learn. My config has changed 6 times in the past month. Each change came from a specific frustration: “the AI keeps doing X, I need to add a rule about X.”

The config is a living document. Treat it like code — iterate, test, refine.