AI 规则持续改进

为什么需要持续改进？

AI 开发规则不是一成不变的。随着项目演进、新模式出现、最佳实践更新，规则也需要不断优化。

Rule Improvement Triggers

When to Create New Rules

Create New Rules When:

A new technology/pattern is used in 3+ files
Common bugs could be prevented by a rule
Code reviews repeatedly mention the same feedback
New security or performance patterns emerge
A complex task requires consistent approach

When to Update Existing Rules

Update Existing Rules When:

Better examples exist in the codebase
Additional edge cases are discovered
Related rules have been updated
Implementation details have changed
User feedback indicates confusion

Analysis Process

1. Pattern Recognition

监控代码库中的重复模式：

// Example: If you see this pattern repeatedly:
const data = await prisma.user.findMany({
	select: { id: true, email: true },
	where: { status: 'ACTIVE' }
});

// Consider documenting:
// - Standard select fields
// - Common where conditions
// - Performance optimization patterns

2. Error Pattern Analysis

追踪常见错误及其解决方案：

Common Error: 'Connection timeout'
Root Cause: Missing strategic delay after service startup
Solution: Add 5-10 second delay after launching services
Rule Update: Add timing guidelines to automation rules

3. Best Practice Evolution

记录最佳实践的演进：

## Before (Old Pattern)

-   Direct DOM manipulation
-   No error handling
-   Synchronous operations

## After (New Pattern)

-   Use framework methods
-   Comprehensive error handling
-   Async/await with proper error boundaries

Rule Quality Framework

Structure Guidelines

每个规则应该遵循以下结构：

# Rule Name

## Purpose

Brief description of what this rule achieves

## When to Apply

-   Specific scenarios
-   Trigger conditions
-   Prerequisites

## Implementation

### Basic Pattern

// Minimal working example

### Advanced Pattern

// Complex scenarios with error handling

## Common Pitfalls

-   Known issues
-   How to avoid them

## References

-   Related rules: [rule-name.md]
-   External docs: [link]

Quality Checklist

发布规则前，确保：

Actionable: Provides clear, implementable guidance
Specific: Avoids vague recommendations
Tested: Examples come from working code
Complete: Covers common edge cases
Current: References are up to date
Linked: Cross-references related rules

Continuous Improvement Workflow

1. Collection Phase

Daily Development

Note repeated code patterns
Document solved problems
Track tool usage patterns

Weekly Review

Analyze git commits for patterns
Review debugging sessions
Check error logs

2. Analysis Phase

Pattern Extraction

# Pseudo-code for pattern analysis
patterns = analyze_codebase()
for pattern in patterns:
    if pattern.frequency >= 3 and not documented(pattern):
        create_rule_draft(pattern)

Impact Assessment

How many files would benefit?
What errors would be prevented?
How much time would be saved?

3. Documentation Phase

Rule Creation Process

Draft initial rule with examples
Test rule on existing code
Get feedback from team
Refine and publish
Monitor effectiveness

4. Maintenance Phase

Regular Updates

Monthly: Review rule usage
Quarterly: Major updates
Annually: Deprecation review

Meta-Rules for Rule Management

Rule Versioning

rule_version: 1.2.0
last_updated: 2024-01-15
breaking_changes:
    - v1.0.0: Initial release
    - v1.1.0: Added error handling patterns
    - v1.2.0: Updated for new framework version

Deprecation Process

## DEPRECATED: Old Pattern

**Status**: Deprecated as of v2.0.0
**Migration**: See [new-pattern.md]
**Removal Date**: 2024-06-01

[Original content preserved for reference]

Rule Metrics

追踪规则的效果：

metrics:
    usage_count: 45
    error_prevention: 12 bugs avoided
    time_saved: ~3 hours/week
    user_feedback: 4.2/5

Integration with Development Workflow

Git Hooks

#!/bin/bash
# pre-commit hook to check rule compliance
./scripts/check-rules.sh

CI/CD Pipeline

# .github/workflows/rules.yml
name: Rule Compliance Check
on: [push, pull_request]
jobs:
    check-rules:
        runs-on: ubuntu-latest
        steps:
            - uses: actions/checkout@v2
            - run: npm run check:rules

IDE Integration

// .vscode/settings.json
{
	"cursor.rules.autoSuggest": true,
	"cursor.rules.path": ".cursor/rules",
	"cursor.rules.checkOnSave": true
}

Best Practices for Rule Evolution

1. Start Simple

Begin with minimal viable rules
Add complexity based on real needs
Avoid over-engineering

2. Learn from Failures

Document what didn't work
Understand why it failed
Share lessons learned

3. Encourage Contributions

Make it easy to suggest improvements
Provide templates for new rules
Recognize contributors

4. Measure Impact

Track before/after metrics
Collect user testimonials
Quantify time savings

总结

持续改进 AI 开发规则是一个迭代过程，需要：

Active monitoring of development patterns
Regular analysis and documentation
Community feedback and collaboration
Systematic maintenance and updates

通过遵循这个指南，团队可以构建一个与代码库一起演进的活知识库。

下一步

学习 PR 多角色审查了解如何从多个角度进行代码审查。

AI 规则持续改进

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