Effective Cursor: Building an Engineering System for AI-Assisted Development

📘 Wiki: https://effective-cursor.vercel.app 

Over the past six months, our team has fully adopted Cursor for two new projects. We evolved from casual “just ask it” usage to a systematic engineering practice. This Wiki distills our hands-on experience, aiming to help you build a truly effective AI-assisted development system.

What Problems Does This Wiki Solve?

Getting started with Cursor is easy—open the chat, describe your needs, accept the code. But turning it into a real productivity multiplier for your team requires solving key challenges:

• Tech Stack Selection: Why does AI perform brilliantly on some projects but struggle on others?
• Engineering Architecture: How do you ensure quality of AI-generated code?
• Rules Writing: Why don’t those “awesome cursorrules” from GitHub actually work?
• Team Workflow: How can the entire team consistently leverage AI’s potential?

Key Insights

1. Choose AI-Friendly Technologies

Not all technologies are equally suited for AI-assisted development. We’ve identified the core characteristics of AI-friendly tech:

💡 TypeScript + React + Tailwind + shadcn/ui is a highly AI-friendly combination.

2. Architecture First, Let AI Run on Rails

Many believe AI can “freestyle” a system—this is a trap.

Without solid engineering foundations, AI tends toward drift, tech debt, or low-quality output.

Just as an executive chef must design the kitchen layout before the team can work efficiently. Our Engineering Architecture section covers how to build the infrastructure for AI-assisted development.

3. Rules: Only Write Project-Specific Requirements

This was our biggest pitfall. The awesome-cursorrules  repo on GitHub is popular, but much of it is noise—“Write clean code”, “Follow best practices”—things LLMs already know.

Valuable Rules are your project’s unique hard requirements:

// ❌ Ineffective Rules
- Use meaningful variable names
- Follow React best practices
 
// ✅ Effective Rules  
- API clients go in `src/clients/`, wrapped with SWR
- Date formatting uses `formatDate()` from `@/lib/date-time-utils.ts`
- Run `npm run lint` and `npm run build` after code generation

See Rules Best Practices for details.

4. Four Workflow Modes, Choose as Needed

Our team identified four Cursor usage patterns:

The Draft-Final mode is our go-to for complex tasks:

1. Draft — Developer quickly captures requirements
2. Final — AI retrieves project context, generates complete technical spec
3. Code — Implement according to the spec

Recommended directory structure:

specs/
├── 001-order-export/
│   ├── draft.md          # Original requirement draft
│   └── final.md          # AI-refined technical spec
├── 002-user-auth/
│   ├── draft.md
│   └── final.md
└── 003-payment-integration/
    ├── draft.md
    └── final.md

Key advantage: Gain full visibility before generating code—typically completing complex tasks in just 3 interactions.

5. Build a Feedback Loop

AI-assisted development requires continuous optimization. We’ve established a metrics framework and retrospective practices to track and improve our team’s AI collaboration effectiveness.

Wiki Overview

We also provide ready-to-use Rules Templates and Prompt Templates.

This Wiki is continuously improving. Star our GitHub repo , and feel free to contribute via Issues or PRs!