Vibe Coding 实战：如何与 AI 高效协作，提升 10 倍编程效率

规划流程

Create a comprehensive plan: Start by working with the AI to write a detailed implementation plan in a markdown file
Review and refine: Delete unnecessary items, mark features as won't do if too complex
Maintain scope control: Keep a separate section for ideas for later to stay focused
Implement incrementally: Work section by section rather than attempting to build everything at once
Track progress: Have the AI mark sections as complete after successful implementation
Commit regularly: Ensure each working section is committed to Git before moving to the next

Use Git religiously: Don't rely solely on the AI tools' revert functionality
Start clean: Begin each new feature with a clean Git slate
Reset when stuck: Use git reset --hard HEAD if the AI goes on a vision quest
Avoid cumulative problems: Multiple failed attempts create layers and layers of bad code
Clean implementation: When you finally find a solution, reset and implement it cleanly

Prioritize high-level tests: Focus on end-to-end integration tests over unit tests
Simulate user behavior: Test features by simulating someone clicking through the site/app
Catch regressions: LLMs often make unnecessary changes to unrelated logic
Test before proceeding: Ensure tests pass before moving to the next feature
Use tests as guardrails: Some founders recommend starting with test cases to provide clear boundaries

Leverage error messages: Simply copy-pasting error messages is often enough for the AI
Analyze before coding: Ask the AI to consider multiple possible causes
Reset after failures: Start with a clean slate after each unsuccessful fix attempt
Implement logging: Add strategic logging to better understand what's happening
Switch models: Try different AI models when one gets stuck
Clean implementation: Once you identify the fix, reset and implement it on a clean codebase

Create instruction files: Write detailed instructions for your AI in appropriate files (cursor.rules, windsurf.rules, claude.md)
Local documentation: Download API documentation to your project folder for accuracy
Use multiple tools: Some founders run both Cursor and Windsurf simultaneously on the same project
Tool specialization: Cursor is a bit faster for frontend work, while Windsurf think slower
Compare outputs: Generate multiple solutions and pick the best one

Create standalone prototypes: Build complex features in a clean codebase first
Use reference implementations: Point the AI to working examples to follow
Clear boundaries: Maintain consistent external APIs while allowing internal changes
Modular architecture: Service-based architectures with clear boundaries work better than monorepos

Established frameworks excel: Ruby on Rails works well due to 20 years of consistent conventions
Training data matters: Newer languages like Rust or Elixir may have less training data
Modularity is key: Small, modular files are easier for both humans and AIs to work with
Avoid large files: Don't have files that are thousands of lines long