Vibe Coding Training

Master the art of AI-assisted development with human oversight. Vibe Coding is GISE's methodology for leveraging AI tools while maintaining code quality, architectural integrity, and developer control.

What is Vibe Coding?

Vibe Coding is a systematic approach to AI-assisted development that:

• Accelerates development speed by 3x while maintaining quality
• Keeps humans in control of architectural and design decisions
• Uses AI for code generation, testing, and documentation
• Maintains consistent code quality through automated gates
• Enables rapid iteration and experimentation

Core Principles

1. Human in Command

• Strategic Decisions: Architecture, design patterns, business logic
• Code Review: Every AI output is reviewed and validated
• Quality Gates: Human oversight on all quality checkpoints

2. AI as Accelerator

• Boilerplate Generation: Let AI handle repetitive code structures
• Test Creation: Generate comprehensive test suites
• Documentation: Auto-generate API docs and code comments

3. Continuous Validation

• Real-time Feedback: Immediate quality checks during development
• Automated Testing: Continuous test execution and validation
• Progressive Integration: Small, validated increments

The Vibe Coding Process

Phase 1: Planning & Context Setting

Planning Checklist:

• Feature requirements clearly defined
• Tasks broken into 30-minute increments
• AI context prepared with relevant code examples
• Quality constraints communicated to AI
• Success criteria established

Phase 2: AI-Assisted Implementation

// Example Vibe Coding Session Flow

// Step 1: Provide context to AI
/*
Context: Working on user authentication service
Architecture: Clean Architecture with TypeScript
Framework: Express.js with inversify DI
Database: PostgreSQL with TypeORM
Testing: Jest with comprehensive coverage

Task: Implement password reset functionality
Requirements:
- Generate secure reset tokens
- Send email notifications
- Expire tokens after 1 hour
- Validate token before reset
*/

// Step 2: Generate initial implementation with AI
// Step 3: Review and refine the generated code
// Step 4: Add comprehensive tests
// Step 5: Validate against quality gates

Phase 3: Review & Refinement

• Code Review: Validate AI-generated code against requirements
• Test Coverage: Ensure comprehensive test coverage
• Performance Check: Validate performance characteristics
• Security Review: Check for security vulnerabilities

Phase 4: Integration & Validation

• Quality Gates: Run linting, type checking, security scans
• Integration Tests: Validate component interactions
• Documentation Update: Generate/update technical documentation
• Knowledge Sharing: Document decisions and patterns

Vibe Coding Sessions

Session Structure (45-60 minutes)

Session Preparation

Environment Setup:

# Pre-session checklist
npm run lint          # Clean slate
npm run type-check    # No type errors
npm test             # All tests passing
git status           # Clean working directory

AI Context Template:

# AI Context for [Feature Name]

## Project Context
- Technology Stack: [TypeScript, Node.js, PostgreSQL, etc.]
- Architecture: [Clean Architecture, Domain-Driven Design]
- Coding Standards: [ESLint config, Prettier, naming conventions]

## Current Task
- Feature: [Specific feature being implemented]
- Requirements: [Detailed requirements]
- Acceptance Criteria: [Success criteria]

## Code Patterns to Follow
- [Existing patterns in the codebase]
- [Specific architectural decisions]
- [Error handling patterns]

## Quality Requirements
- Test coverage > 90%
- No TypeScript 'any' types
- Comprehensive error handling
- Proper logging with context

Best Practices During Sessions

AI Prompt Engineering:

# Effective Prompt Structure

You are an expert [technology] developer working on [project description].

Context:
[Provide relevant context about the codebase, architecture, and patterns]

Task:
[Clear, specific task description]

Requirements:
- [Specific requirement 1]
- [Specific requirement 2]
- Follow existing code patterns
- Include comprehensive error handling
- Add proper TypeScript types
- Generate corresponding tests

Constraints:
- [Any limitations or constraints]
- [Performance requirements]
- [Security considerations]

Advanced Techniques

Context-Aware Generation

// Provide AI with relevant context from your codebase
// Include similar implementations as examples
// Reference existing patterns and conventions

// Example: Before generating a new service, show AI existing services
// This helps maintain consistency across your codebase

Iterative Refinement

Quality Validation

// Automated quality validation during Vibe Coding
const qualityGates = {
  linting: () => exec('npm run lint'),
  typeCheck: () => exec('npm run type-check'),
  tests: () => exec('npm run test'),
  coverage: () => exec('npm run test:coverage'),
  security: () => exec('npm run security-audit')
};

// Run after each AI generation cycle
const validateQuality = async () => {
  for (const [gate, check] of Object.entries(qualityGates)) {
    const result = await check();
    if (!result.success) {
      console.log(`❌ ${gate} failed: ${result.error}`);
      return false;
    }
  }
  return true;
};

Common Patterns & Anti-Patterns

✅ Good Vibe Coding Practices

Clear Context Setting:

// Good: Provide clear context and examples
/*
Context: User service following repository pattern
Example: UserService.findById(id) returns Promise<User | null>
Task: Implement UserService.updateProfile(userId, profileData)
Follow same error handling and logging patterns
*/

Incremental Development:

// Good: Build incrementally with validation
// 1. Generate basic structure
// 2. Add error handling  
// 3. Add comprehensive tests
// 4. Add logging and monitoring
// 5. Validate and integrate

❌ Vibe Coding Anti-Patterns

Blind Acceptance:

// Bad: Using AI code without review
const generatedCode = await ai.generate(prompt);
// ❌ Don't commit without understanding and reviewing

Context-Free Generation:

// Bad: Vague prompts without context
"Create a user service"
// ❌ Too vague, will generate generic code

Large Batch Generation:

// Bad: Generating too much code at once
"Generate entire authentication system"
// ❌ Hard to review and validate effectively

Measuring Success

Productivity Metrics

• Development Velocity: Lines of quality code per hour
• Feature Completion Rate: Features delivered per sprint
• Time to Market: Days from requirements to production

Quality Metrics

• Bug Density: Bugs per 1000 lines of code
• Test Coverage: Percentage of code covered by tests
• Code Review Efficiency: Time spent on code reviews

Developer Experience

• Learning Curve: Time to onboard new team members
• Developer Satisfaction: Team satisfaction with development process
• Flow State: Percentage of time in productive flow

Tools & Setup

Recommended AI Tools

Tool	Best For	Integration	Cost
Cursor	Complete development workflow	Native IDE	Paid
GitHub Copilot	Code completion	VS Code, JetBrains	Paid
ChatGPT/Claude	Complex problem solving	Browser/API	Free/Paid
Codeium	Code generation	Multiple IDEs	Free

IDE Configuration

// VS Code settings for Vibe Coding
{
  "github.copilot.enable": {
    "typescript": true,
    "javascript": true,
    "markdown": true
  },
  "editor.inlineSuggest.enabled": true,
  "editor.quickSuggestions": {
    "comments": true,
    "strings": true,
    "other": true
  },
  "typescript.preferences.includePackageJsonAutoImports": "on"
}

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Next Steps:

• Practice Session: Try a 45-minute Vibe Coding session on a small feature
• Team Training: Train your team on Vibe Coding principles
• Tool Setup: Configure your development environment for optimal AI assistance

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Remember: Vibe Coding is about enhancing human capability, not replacing human judgment. The AI accelerates implementation while you maintain control over architecture and quality.