AI Coding Guide

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F. Learning Roadmap ​

How to Use This Guide ​

This chapter provides customized learning paths based on different roles and learning objectives. Each path is carefully designed to ensure you master core AI-assisted programming skills in the most efficient way.

Selection advice:

  • If you're a beginner, it's recommended to complete the "Quick Start" path first, then choose the corresponding professional path based on your career direction
  • If you want systematic in-depth learning, you can choose the "Deep Learning" path
  • If you have limited time, you can jump directly to chapters most relevant to your work

Path 1: Frontend Developer 🎨 ​

Suitable for: React/Vue/Angular developers, UI/UX engineers

Learning objectives: Master using AI tools to rapidly develop modern frontend applications, improve component development and debugging efficiency

Recommended learning sequence:

Phase 1: Basic Cognition (1-2 days) ​

  1. Chapter 1: Introduction to AI Programming - Understand basic concepts of AI-assisted programming
  2. Chapter 2: Tool Selection - Choose AI tools suitable for frontend development (recommended: Cursor, GitHub Copilot)
  3. Chapter 3: Prompt Engineering Basics - Learn how to write effective prompts

Phase 2: Practical Skills (3-5 days) ​

  1. Chapter 4: Code Generation - Focus on component generation, style generation
  2. Chapter 5: Code Completion and Refactoring - Master auto-completion and code optimization techniques
  3. Chapter 7: UI/UX Development - Learn to use AI to quickly implement design mockups
  4. Chapter 8: Testing and Debugging - Learn automated test generation (Jest, Vitest)

Phase 3: Advanced Optimization (2-3 days) ​

  1. Chapter 10: Performance Optimization - Learn to use AI to analyze and optimize frontend performance
  2. Chapter 13: Multilingual Development - If involved in internationalization projects
  3. Appendix B: Prompt Template Library - Bookmark commonly used frontend development templates

Practical project suggestions:

  • Use AI to assist in developing a complete React/Vue component library
  • Refactor a functional module of an existing project with AI
  • Use AI to generate responsive layouts and animation effects

Path 2: Backend Developer ⚙️ ​

Suitable for: Node.js/Python/Java/Go backend engineers, API developers

Learning objectives: Use AI tools to improve API development, database design, and system architecture capabilities

Recommended learning sequence:

Phase 1: Basic Preparation (1-2 days) ​

  1. Chapter 1: Introduction to AI Programming
  2. Chapter 2: Tool Selection - Choose tools supporting backend languages
  3. Chapter 3: Prompt Engineering Basics

Phase 2: Core Skills (4-6 days) ​

  1. Chapter 4: Code Generation - Focus on API endpoints, data model generation
  2. Chapter 5: Code Completion and Refactoring - Learn to refactor complex business logic
  3. Chapter 6: Documentation Generation - Auto-generate API documentation (OpenAPI/Swagger)
  4. Chapter 9: Database and SQL - Learn database design, query optimization
  5. Chapter 8: Testing and Debugging - Unit testing, integration testing generation

Phase 3: System Design (3-4 days) ​

  1. Chapter 11: Architecture Design - Use AI for system architecture design
  2. Chapter 10: Performance Optimization - Backend performance analysis and optimization
  3. Chapter 12: Security Best Practices - Learn secure coding and vulnerability fixing
  4. Chapter 14: DevOps Integration - CI/CD process optimization

Practical project suggestions:

  • Use AI to design and implement a RESTful API service
  • Use AI to optimize existing project database query performance
  • Let AI help you refactor a complex business logic module

Path 3: Full-Stack Developer 🚀 ​

Suitable for: Full-stack engineers, indie developers, startup technical leads

Learning objectives: Comprehensively master AI-assisted full-stack development, from frontend to backend, from development to deployment

Recommended learning sequence:

Phase 1: Global Cognition (2-3 days) ​

  1. Chapter 1: Introduction to AI Programming
  2. Chapter 2: Tool Selection - Choose comprehensive tools supporting multiple languages
  3. Chapter 3: Prompt Engineering Basics
  4. Chapter 11: Architecture Design - Establish systems thinking first

Phase 2: Frontend Skills (3-4 days) ​

  1. Chapter 4: Code Generation - Frontend component development
  2. Chapter 7: UI/UX Development
  3. Chapter 5: Code Completion and Refactoring

Phase 3: Backend Skills (3-4 days) ​

  1. Chapter 4: Code Generation - Backend API development (reread, focus on backend part)
  2. Chapter 9: Database and SQL
  3. Chapter 12: Security Best Practices

Phase 4: Integration and Deployment (2-3 days) ​

  1. Chapter 8: Testing and Debugging - Full-stack testing strategy
  2. Chapter 14: DevOps Integration
  3. Chapter 10: Performance Optimization - Full-stack performance optimization

Phase 5: Advanced Topics (3-4 days) ​

  1. Chapter 6: Documentation Generation
  2. Chapter 13: Multilingual Development
  3. Chapter 15: Team Collaboration
  4. Appendix A: Tool Comparison Matrix - Choose best tool combination for team

Practical project suggestions:

  • Use AI to develop a complete SaaS application from scratch
  • Use AI to refactor frontend and backend architecture of an old project
  • Let AI help you implement complete process from development to deployment

Path 4: DevOps / SRE 🔧 ​

Suitable for: Operations engineers, SRE, platform engineers

Learning objectives: Use AI to optimize deployment processes, infrastructure management, and system monitoring

Recommended learning sequence:

Phase 1: Basic Understanding (1 day) ​

  1. Chapter 1: Introduction to AI Programming
  2. Chapter 2: Tool Selection - Choose tools supporting scripts and configuration files
  3. Chapter 3: Prompt Engineering Basics

Phase 2: Core Areas (3-4 days) ​

  1. Chapter 14: DevOps Integration - Core chapter, focus on learning
  2. Chapter 4: Code Generation - Focus on script, configuration file generation
  3. Chapter 12: Security Best Practices - Infrastructure security
  4. Chapter 10: Performance Optimization - System performance monitoring and optimization

Phase 3: Practical Application (2-3 days) ​

  1. Chapter 9: Database and SQL - Database operations
  2. Chapter 8: Testing and Debugging - Infrastructure testing
  3. Chapter 6: Documentation Generation - Operations documentation automation
  4. Chapter 15: Team Collaboration - DevOps culture and collaboration

Phase 4: Advanced Topics (2 days) ​

  1. Chapter 11: Architecture Design - Understand cloud architecture design
  2. Chapter 13: Multilingual Development - If need to maintain multilingual projects

Practical project suggestions:

  • Use AI to write Kubernetes configuration files and Helm Charts
  • Let AI help you optimize CI/CD Pipeline
  • Use AI to generate monitoring alert rules and automated response scripts

Path 5: Product Manager 📊 ​

Suitable for: Product managers, project managers, technical PMs

Learning objectives: Understand AI programming capability boundaries, better collaborate with technical teams, quickly validate product prototypes

Recommended learning sequence:

Phase 1: Concept Understanding (1 day) ​

  1. Chapter 1: Introduction to AI Programming - Understand possibilities and limitations of AI programming
  2. Chapter 2: Tool Selection - Understand tools team can use
  3. Chapter 3: Prompt Engineering Basics - Learn how to communicate with AI

Phase 2: Practical Skills (2-3 days) ​

  1. Chapter 7: UI/UX Development - Learn rapid prototyping
  2. Chapter 4: Code Generation - Understand feature development complexity
  3. Chapter 6: Documentation Generation - Auto-generate requirements docs, API docs
  4. Chapter 15: Team Collaboration - How to promote AI tools in teams

Phase 3: Technical Insights (2 days) ​

  1. Chapter 11: Architecture Design - Understand technical architecture decisions
  2. Chapter 10: Performance Optimization - Understand performance metrics and optimization directions
  3. Chapter 12: Security Best Practices - Understand security requirements

Phase 4: Strategic Thinking (1 day) ​

  1. Chapter 16: Future Trends - Understand future directions of AI programming
  2. Appendix E: 2026 Annual Buzzwords - Maintain technical sensitivity

Practical project suggestions:

  • Use AI tools to build product prototypes yourself to validate ideas
  • Use AI to generate product requirement documents and technical specifications
  • Learn to use AI tools to communicate more efficiently with development teams

Path 6: Quick Start (3-day crash course) ⚡ ​

Suitable for: All developers wanting to quickly get started with AI programming

Learning objectives: Master core skills of AI-assisted programming in 3 days, immediately boost work efficiency

Day 1: Basic Cognition and Tool Preparation ​

  • Morning (2-3 hours)
    • Chapter 1: Introduction to AI Programming (understand basic concepts)
    • Chapter 2: Tool Selection (choose and install a tool)
  • Afternoon (2-3 hours)
    • Chapter 3: Prompt Engineering Basics (focus on learning)
    • Practice: Write 10 prompts for different scenarios

Day 2: Core Skills Practice ​

  • Morning (3 hours)
    • Chapter 4: Code Generation (focus on learning, practice more)
    • Practice: Generate a complete small functional module
  • Afternoon (2-3 hours)
    • Chapter 5: Code Completion and Refactoring
    • Chapter 8: Testing and Debugging (quick browse)
    • Practice: Refactor a piece of your own code

Day 3: Advanced Techniques and Best Practices ​

  • Morning (2-3 hours)
    • Chapter 7: UI/UX Development or Chapter 9: Database and SQL (choose one based on your direction)
    • Practice: Complete a small practical project
  • Afternoon (2 hours)
    • Chapter 15: Team Collaboration (quick browse)
    • Appendix B: Prompt Template Library (bookmark for later)
    • Summary and review

After 3 days you will be able to:

  • Proficiently use AI tools to write code
  • Improve development efficiency by at least 30%
  • Independently solve common problems in daily development

Path 7: Deep Learning (Systematic Complete Learning) 🎓 ​

Suitable for: Developers wanting to systematically master all AI programming skills, technical team leaders

Learning objectives: Complete learning of all content in this guide, become an AI-assisted programming expert

Recommended learning sequence (estimated 3-4 weeks):

Week 1: Basics and Core Skills ​

  1. Chapter 1: Introduction to AI Programming
  2. Chapter 2: Tool Selection
  3. Chapter 3: Prompt Engineering Basics
  4. Chapter 4: Code Generation
  5. Chapter 5: Code Completion and Refactoring
  6. Chapter 6: Documentation Generation

This week's task: Complete 5 small practical projects, each using different AI tools

Week 2: Professional Domain Deepening ​

  1. Chapter 7: UI/UX Development
  2. Chapter 8: Testing and Debugging
  3. Chapter 9: Database and SQL
  4. Chapter 10: Performance Optimization

This week's task: Use AI to refactor core modules of a medium-sized project

Week 3: Architecture and Engineering ​

  1. Chapter 11: Architecture Design
  2. Chapter 12: Security Best Practices
  3. Chapter 13: Multilingual Development
  4. Chapter 14: DevOps Integration

This week's task: From scratch, use AI to design and implement a complete system

Week 4: Team and Strategy ​

  1. Chapter 15: Team Collaboration
  2. Chapter 16: Future Trends
  3. Appendix A: Tool Comparison Matrix
  4. Appendix B: Prompt Template Library
  5. Appendix C: Common Questions
  6. Appendix D: Resource List
  7. Appendix E: 2026 Annual Buzzwords

This week's task:

  • Develop team AI programming promotion plan
  • Write team prompt standards and best practices documentation
  • Share learning insights, drive team growth

After completion you will be able to:

  • Become team's AI programming expert and promoter
  • Deeply understand capability boundaries and best practices of AI programming
  • Develop AI tool and process standards suitable for team

Learning Path Visualization ​

The flowchart below shows the relationships between different learning paths and recommended routes:

Learning Recommendations ​

1. Develop Personalized Plan ​

  • Adjust learning pace based on your current skill level
  • Don't skip basic chapters (1-3), they are foundation for subsequent learning
  • Flexibly adjust learning focus based on actual project needs

2. Learn While Practicing ​

  • After completing each chapter, immediately apply in actual projects
  • Build your own prompt template library
  • Record learning notes and best practices

3. Continuous Iteration ​

  • AI tools develop rapidly, regularly review and update knowledge
  • Follow resources in Appendix D, maintain learning
  • Join communities, exchange experiences with other developers

4. Evaluate Results ​

  • Complete at least 3 actual projects using knowledge from this guide
  • Measure actual improvement in development efficiency (recommended target: 30-50%)
  • Team learning can organize regular sharing sessions

Next Steps ​

After choosing a learning path that suits you, it's recommended to:

  1. First step: Go to Chapter 1: 3-Minute AI Experience to start learning
  2. Tool preparation: Check Chapter 5: AI Programming Tools Landscape to choose suitable tools
  3. Practice exercises: Use Appendix C: Resource Index to get more learning materials
  4. Problem solving: Check Appendix D: FAQ when encountering issues

Remember: AI is an assistive tool, core programming abilities and systems thinking are still most important. Happy learning! 🚀

An AI coding guide for IT teams

MIT License