What to Put in My Teams Cursor Rules File

Jun 26, 2025

June 26, 2025

As AI-powered coding assistants like Cursor become more integrated into our development workflows, guiding their output effectively is key to maximizing their benefits. A Cursor Rules file is a powerful mechanism for this, acting as a set of persistent, system-level instructions that inform the AI about your project's specific patterns, best practices, and constraints. There's a black market for quality Cursor rules—everyone wants the best, but nobody knows if theirs are any good. Since there aren't many established authorities on AI coding assistant best practices yet, the real knowledge gets shared in YC group chats, one-on-one conversations, and through trial and error. After crowdsourcing insights from multiple YC groups, here's what actually works.

Key Information for Your Cursor Rules File

Coding Standards and Best Practices

Think of Cursor rules as onboarding documentation for a new engineer. A great engineer only needs to be told something once—they learn it, internalize it, and apply it going forward. Your rules file should clearly define the coding conventions using direct, positive instructions.

Write what TO do, not what NOT to do. This is critical—AI systems struggle with negative instructions and may actually do the thing you're telling them not to do.

Style Guides: Specify formatting preferences (e.g., indentation, line length, use of semicolons). If you use a linter or formatter like Prettier or ESLint, mention its configuration or key rules.
Naming Conventions: Define how variables, functions, classes, and components should be named (e.g., camelCase for variables, PascalCase for classes).
Preferred Patterns: "Use functional components with hooks for all React components" rather than "avoid class components." Explicitly state preferred coding patterns and, just as importantly, anti-patterns to avoid[2]. For example, you might prefer functional components over class components in React, or specific ways to handle state management.
Function Structure: "Include only one return statement per function" instead of "don't return early"
Deprecated Practices: Include reminders about practices or library features that are deprecated or should no longer be used in your project[3].

Project-Specific Context

Providing context about your project's architecture and dependencies is crucial for relevant AI suggestions[2].

Project Structure: Briefly describe the layout of your directories and the purpose of key folders (e.g., src/components, src/utils, src/api). This helps the AI understand where new code should go or where existing modules can be found[2].
Dependencies and Versions: Explicitly specify important libraries, frameworks, and SDK versions[2]. This prevents the AI from using outdated syntax or features from incorrect versions.
- Example: Use React 18.x syntax. Do not use class components. For state management, prefer Zustand over Redux.
Unique Requirements: Detail any specific configurations, architectural decisions, or constraints unique to the project, such as custom build steps, internationalization (i18n) patterns, or accessibility (a11y) requirements[2].

Code Examples and Edge Cases

Concrete examples are often more effective than abstract descriptions[2].

Include examples for common patterns in your codebase—API calls, component structure, error handling, state management. Concrete examples work much better than abstract descriptions.

Things like:

// Example: Error Handling for API calls
// Always use try-catch blocks for asynchronous operations that might fail.
// Log errors using the 'logger.error()' utility.
// For user-facing errors, provide a clear message and a reference ID.

async function fetchUserProfile(userId: string): Promise<UserProfile | null> {
  try {
    const response = await apiClient.get(`/users/${userId}`);
    return response.data;
  } catch (error) {
    logger.error(`Failed to fetch profile for user ${userId}:`, error);
    // Display a user-friendly error message via notification service
    showNotification('Error fetching profile. Please try again later.', 'error');
    return null;
  }
}

Rationale Behind Patterns

Briefly explain why certain patterns are preferred. This helps the AI make intelligent decisions when adapting examples to new contexts.

"Use our internal data-fetcher library for API calls because it automatically handles retries, caching, and authentication tokens"
"Prefer Zustand over Redux because it requires less boilerplate and integrates better with our component structure"
"Use Tailwind classes instead of CSS modules to maintain consistency with our design system"

Best Practices for Crafting Effective Rules

How you write and manage your rules file also impacts its effectiveness.

Keep it Concise and Focused

Create multiple small, specific files rather than one massive file. This prevents polluting the context window with irrelevant information and keeps rules targeted to specific languages or modules.

Cursor offers four modes for including rules:

Auto-attached based on file type (best choice)
Agent-requested when relevant (second best)
Include on request (worst—nobody remembers to request it)
Always include (bad unless genuinely universal)

Keep things language or folder-specific. Your React rules shouldn't interfere when writing Python scripts.

Be Specific and Provide Concrete Examples

Vague instructions like "write clean code" are unhelpful. Instead, define what "clean code" means in your context with specific criteria and examples[1].
The more concrete and illustrative your examples, the better the AI can understand and adhere to your desired patterns[1].

Update Rules Based on Feedback

The most important habit: Update your rules every time the AI does something wrong. The key indicator of ineffective rules is repetitive feedback—if you keep saying "nope, don't do it that way" or "you forgot about this," that's a symptom of inadequate rules.

Use Cursor's "generate cursor rules" feature whenever you give feedback during a chat. The generated rules aren't perfect—remove about 80% of the random stuff—but it's a solid first draft. If you don't want to say something again, turn it into a rule.

Centralize Your Guidelines

Make your Cursor rules the single source of truth that both humans and AI learn from. Don't maintain separate code quality documents—consolidate everything into these files so there's no confusion about standards.

Boosting Developer Productivity with Cursor Rules

The Impact of Well-Defined Rules

This isn't about marginal improvements—it's transformational. AI can be 5x better when you invest upfront in giving it the right guidance. This is a one-time cost that gets compounding returns.

With effective rules, you spend less time on repetitive corrections and can focus on actual task-specific work. You need less effort crafting perfect prompts because the AI already understands your codebase conventions.

Without good rules, you're re-onboarding the AI every single conversation. With them, you pay once and benefit continuously across your entire team.

Measuring and Optimizing Productivity

While Cursor Rules directly refine AI assistance, understanding their full impact on team efficiency and identifying areas for further improvement benefits from broader analytics. Developer productivity tools that offer insights into engineering work can help track team output, reveal hidden strengths and weaknesses, monitor time investments, and debug project delivery bottlenecks for engineering teams of all sizes. By integrating such analytics, teams can better quantify the productivity gains from well-configured AI assistants guided by robust rules files and pinpoint other opportunities to optimize their development processes and AI integration strategies.

Maximize Your AI Coding Assistant

Investing the effort to create and maintain a comprehensive rules file is a crucial step in leveraging AI coding assistants to their full potential. By providing clear, specific, and context-aware guidance, you empower the AI to be a more effective partner, generating code that is not only functional but also high-quality, consistent, and aligned with your project's unique needs. This ultimately translates to saved time, improved codebases, and a more productive development experience for the entire team.