Hey Cursor community! 
I wanted to share our experience implementing Cursor across our development team and some strategies we’ve developed to improve code generation adoption rates and overall developer experience. Would love to hear your thoughts and suggestions!
## Our Setup & Context
We’re a team of ~20 developers maintaining several large-scale enterprise Java projects:
- **Codebase size**: 100k - 400k lines each
- **Tech stack**: Spring Cloud ecosystem (SpringBoot, MyBatis-Plus, OpenFeign, etc.)
- **Main language**: Java 1.8
- **Architecture**: Microservices with typical Controller → Service → Repository → Mapper layers
## The Challenge
We wanted to achieve two main goals:
1. **Increase code generation adoption rate** - Make Cursor generate more accurate code that requires fewer modifications and better aligns with our existing business logic
2. **Improve team UX** - Streamline operations while maintaining or improving adoption rates
## Our Implementation Strategy
### 1. Repository-Level Cursor Rules
In each project repository, we’ve implemented two core rule files:
**`baserule.mdc`** - Project-specific development standards including:
- Mandatory code architecture layers and calling relationships
- Logging standards (when/how to log, appropriate log levels)
- Date/time handling conventions (always use LocalDateTime, timezone settings)
- SQL operation standards (no `select *`, update only necessary fields)
- Collection initialization best practices
- Exception handling guidelines
**`system-prompt.mdc`** - AI role definition that establishes:
- Role as a senior engineer and proactive development assistant
- Progressive development principles (5-phase approach)
- Quality check requirements (compilation verification, dependency checks)
- Absolutely prohibited behaviors (no TODO markers, no incomplete implementations)
### 2. Project Knowledge Base Structure
We’ve created a comprehensive knowledge base system:
```
project_root/
├── ai-knowledge-base/
│ ├── base-knowledge-summary.md # Overall knowledge base guide
│ ├── main-tags-glossary.md # Basic tag vocabulary
│ ├── main-table-structure.md # Core table structures
│ ├── code-generate-example.md # Code generation examples
│ ├── mq-operation-example.md # MQ operation examples
│ ├── moduleA/ # Module-specific knowledge
│ │ ├── moduleA-knowledge-summary.md
│ │ ├── moduleA-tags-glossary.md
│ │ └── moduleA-table-structure.md
│ └── private/ # Developer personal knowledge
│ └── developer.md
```
**Why we created this knowledge base:**
- We want the AI to read relevant project context before completing tasks
- It contains project-wide explanations and module-specific documentation
- Provides consistent terminology and business domain understanding
**Key knowledge base components:**
- **Tag-based search system**: Uses hashtag prefixes (e.g., `#user`, `#order`) for efficient knowledge retrieval
- **Table structure documentation**: Complete schema definitions with relationship explanations
- **Code generation templates**: Standardized patterns for Entity, Service, Controller generation
- **MQ operation examples**: Standardized message producer/consumer patterns following our base classes
### 3. Team Usage Guidelines
We’ve established these team conventions:
1. **Use Markdown syntax** in prompts for better structure and AI comprehension
2. **Break complex tasks** into smaller modules with focused prompts for each
3. **Reference knowledge base** when working on unfamiliar modules or business logic
## What We’re Seeing
**Positive outcomes:**
- More consistent code generation that follows our architectural patterns
- Reduced back-and-forth for code corrections
- Better integration with existing business logic
- Faster onboarding for new team members
**Current questions:**
- How do others structure large project knowledge bases?
- Any best practices for maintaining rule files as projects evolve?
- Techniques for measuring adoption rate improvements?
## Questions for the Community
1. **Do you think our approach is sound?** Any suggestions for improvement?
2. **Cursor Rules best practices?** What have you found most effective in your rule files?
3. **Project knowledge base necessity?** Is this level of documentation overkill, or have you found similar approaches valuable?
4. **Enterprise Java specifics?** Any Java/Spring-specific Cursor optimizations you’d recommend?
## Sample Rule Files
Here are excerpts from our actual rule files:
**baserule.mdc highlights:**
- Enforced 4-layer architecture (Controller → Service → Repository → Mapper)
- Mandatory logging at external access points and third-party integrations
- BigDecimal requirement for all floating-point calculations
- Collection initialization with explicit sizing
- Guard clause patterns over nested if-else structures
**system-prompt.mdc highlights:**
- 5-phase progressive development (basic classes → data access → business logic → integration → access points)
- Compilation verification after each phase
- Prohibition of incomplete implementations or TODO markers
- Self-check requirements before output
The knowledge base includes comprehensive examples of:
- Entity/Enum generation from table schemas
- Service layer CRUD operations
- MQ producer/consumer patterns using our base classes
- Controller implementations with proper Swagger annotations
## Looking Forward
We’re continuously refining this approach based on team feedback. The goal is to make Cursor feel like a team member who understands our codebase, conventions, and business context.
What strategies have you found effective for enterprise-scale Cursor adoption? Any pitfalls we should watch out for?
Thanks for reading, and looking forward to your insights! 
-–
**TL;DR**: We’ve implemented structured Cursor rules + comprehensive project knowledge bases for our 20-person Java team. Seeing improved code generation accuracy and developer experience. Looking for community feedback and best practices!