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Teach Cursor Your Codebase

Published:

If you’ve been using Cursor or similar AI coding tools, you’ve probably noticed that their suggestions don’t always align with your project’s patterns and conventions. The good news? With the right context, you can transform Cursor from a generic code assistant into a teammate that truly understands your codebase.

The Gist: Context is Everything

While Cursor does its best to understand your code by analyzing nearby files, this approach has limitations. When working with larger codebases, it can miss important patterns or suggest solutions that don’t quite fit your architecture. The solution? Strategic documentation that helps Cursor understand not just what your code does, but how it works together.

What Cursor Needs to Know

To get the most out of Cursor, you’ll want to provide clear information about:

  • Your Project’s Architecture:
    Core patterns, component relationships, and the reasoning behind key design decisions
  • Code Organization:
    Directory structure, significant files, and implementation conventions
  • Project Standards:
    Coding style, error handling patterns, testing approaches, and documentation requirements

By capturing this information in Markdown and storing it alongside your codebase, you create a reliable reference that Cursor can use to generate more contextually appropriate code.

Your Documentation Template

Here’s a structured template you can use to document your codebase effectively:

# Codebase Documentation Template

## Architecture Overview

### Core Components
- List major architectural components (3-5)
- Brief explanation of how they interact
- Key design decisions and their rationale

### Key Design Patterns
1. **Pattern One**
   ```
   Structural example or pseudo-code
   showing pattern implementation
   ```
   - Use cases
   - Implementation details
   - Common variations

2. **Pattern Two**
   ```
   Structural example or pseudo-code
   ```
   - Use cases
   - Implementation details
   - Common variations

[Additional patterns as needed]

## Code Organization

### Directory Structure
```
project_name/
├── component1/            # Brief description
│   ├── submodule1/       # Purpose
│   └── submodule2/       # Purpose
├── component2/           # Brief description
└── tests/               # Test organization
```

### Key Files and Their Significance
1. **Core File/Module One**
   - Primary responsibility
   - Key features
   - Dependencies

2. **Core File/Module Two**
   - Primary responsibility
   - Key features
   - Dependencies

## Style Guide and Conventions

### Code Style
1. **Language-Specific Patterns**
   - Key conventions
   - Examples of proper usage
   ```
   Example code snippet
   demonstrating convention
   ```

2. **Project-Specific Patterns**
   - Custom conventions
   - Implementation requirements
   - Examples

### Documentation Conventions
1. **Code Documentation**
   - Format
   - Required elements
   - Examples

2. **API Documentation**
   - Format
   - Required elements
   - Examples

## Best Practices

### Testing
1. **Test Organization**
   - Structure
   - Naming conventions
   - Coverage requirements

2. **Test Patterns**
   - Common patterns
   - Examples
   - Edge cases

### Error Handling
1. **Error Patterns**
   - Standard approaches
   - Examples
   ```
   Error handling example
   ```

2. **Recovery Strategies**
   - Recommended approaches
   - Examples

## Integration Points

### External Services
1. **Service One**
   - Integration method
   - Configuration requirements
   - Error handling

2. **Service Two**
   - Integration method
   - Configuration requirements
   - Error handling

### Internal Services
1. **Service One**
   - Purpose
   - Dependencies
   - Usage patterns

## Common Patterns and Idioms

### Pattern Category One
```python
# Example implementation
class PatternExample:
    def example_method(self):
        pass
```
- Use cases
- Implementation notes
- Common variations

### Pattern Category Two
```python
# Example implementation
def pattern_example():
    pass
```
- Use cases
- Implementation notes
- Common variations

## Noteworthy Features

1. **Feature One**
   - Purpose
   - Implementation details
   - Usage examples

2. **Feature Two**
   - Purpose
   - Implementation details
   - Usage examples

## Common Pitfalls and Solutions

1. **Pitfall Category One**
   - Description
   - Warning signs
   - Solutions/workarounds

2. **Pitfall Category Two**
   - Description
   - Warning signs
   - Solutions/workarounds

## Future Enhancement Areas

1. **Area One**
   - Current limitations
   - Proposed improvements
   - Implementation considerations

2. **Area Two**
   - Current limitations
   - Proposed improvements
   - Implementation considerations

## Notes for LLMs

### Context Requirements
- Essential context needed for understanding
- Key assumptions
- Critical limitations

### Interaction Guidelines
1. **Code Generation**
   - Style requirements
   - Pattern requirements
   - Common pitfalls

2. **Code Analysis**
   - Important considerations
   - Key areas to check
   - Common issues

### Response Formatting
1. **Code Examples**
   - Style guidelines
   - Required elements
   - Format requirements

2. **Explanations**
   - Detail level
   - Required context
   - Format requirements

## Metadata
- Last Updated: [Date]
- Version: [Version number]
- Key Contributors: [List]
- Documentation Format Version: [Version]

Documentation Setup

Here’s a straightforward approach to getting started:

  1. mkdir docs - create a documentation directory
  2. touch docs/overview.md - establish your main documentation file
  3. Use code2prompt to generate initial documentation

Best practices for code2prompt:

  • Exclude non-essential files like package-lock.json
  • Process larger codebases in manageable segments
  • Leverage more capable models (Claude 3.5 Sonnet or GPT-4) for better documentation quality

Putting It Into Practice

With your documentation in place, here are three effective ways to enhance Cursor’s understanding of your codebase:

  1. Direct Context:
    Include relevant documentation when seeking Cursor’s assistance with complex tasks
  2. Smart Selection:
    Combine code selections with corresponding documentation when requesting help
  3. Cursor Rules:
    Create rules that automatically incorporate documentation context for consistent results

The more effectively Cursor understands your codebase, the more valuable its suggestions become. Think of it as an investment in better, more consistent code generation.

Additional Resources