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🚀 AI-Powered Coding Tools: Best Practices & Mastery Guide


📋 Table of Contents


🛡️ Universal Best Practices

The following principles apply to all AI-assisted coding tools.
They help you leverage AI effectively without sacrificing code quality, security, or architectural consistency.

1. 🎯 Effective Prompting (The Most Critical Skill)

  • Be specific and constrained
    Avoid vague prompts. Clearly describe what you want, how it should be done, and within which constraints.

    ❌ “Refactor this code”
    ✅ “Refactor this function to use async/await, add input validation, and apply TypeScript generics.”

  • Define the expected output
    Examples:

    • “Generate unit tests using Jest
    • “Return a Mermaid class diagram
    • “Output only the code diff, no explanation”
  • Iterate instead of over-prompting
    Start simple, review the output, then refine.
    AI works best in short feedback loops, not one giant prompt.


2. 📚 Context Is Everything

AI can only produce high-quality results when it understands the full context.

  • Explicitly state technical constraints

    • Frameworks (React Hooks, Spring Boot, FastAPI)
    • Libraries (Zod, Prisma, Pandas)
    • Internal conventions (naming, logging, error handling)
  • Reference related code Do not expect the AI to infer your architecture.

    Example:
    “This new endpoint must follow the same error handling pattern as UserService.ts.”

  • Explain intent and business logic Tell the AI why the code exists, not just what to write.


3. 🛡️ Verification and Accountability (Non-Negotiable)

  • Never commit blindly
    AI output should be treated as a draft, not final production code.

  • Test everything Especially for:

    • Authentication & authorization
    • Data validation
    • Performance-critical paths
  • AI accelerates — it does not replace expertise
    If you don’t fully understand the generated code, you shouldn’t ship it.


🧰 Supported Tools & IDEs

Tool / IDE Description
Cursor AI-first code editor with strong full-repo context
Claude Code CLI-based AI coding assistant for large tasks
GitHub Copilot Real-time AI pair programmer
Devin Autonomous AI software engineer
Windsurf AI-enhanced editor focused on workflow efficiency
Kiro AI-powered development environment for rapid prototyping
Antigravity Lightweight AI assistant for code generation and refactoring
Replit AI Cloud-based IDE with built-in AI and deployment
Zed Editor High-performance collaborative editor with AI support
Lovable AI platform for building and deploying web applications
Bolt.new Instant AI-driven web app generation
TRAE AI-integrated editor for coding and debugging
v0 AI-driven UI and full-stack prototyping tool (Vercel)
Manus Autonomous AI agent for project-level execution
Qoder AI-powered editor with intelligent suggestions
Tabnine Privacy-focused AI code completion
JetBrains IDEs Full IDE suite with AI plugins
Codeium Free AI code completion and chat
Sider.AI AI-powered code review and security analysis
Other AI Tools Prompts, rules, agents, and templates

🔗 Best Practices & Learning Resources

General AI Coding & Agent Resources


Cursor


Claude Code

Templates:

Prompts:

Agents:

Skills:


GitHub Copilot


Windsurf


TRAE


Learning Resources

Articles, guides, and references for learning AI-assisted development.


Contributing

Contributions are welcome!

Please ensure:

  • Links are relevant and maintained
  • Descriptions are concise and neutral
  • No duplicate or promotional entries

Open an issue or submit a pull request.


License

This list is licensed under the MIT License.

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