🤖 GitHub Copilot Certification Guide (GH-300) - AI Development Mastery

// Basic Copilot interaction patterns
// Comment-driven development example
function calculateCompoundInterest(
  principal: number,
  rate: number, 
  time: number,
  compoundFrequency: number = 1
): number {
  // Calculate compound interest using A = P(1 + r/n)^(nt)
  // where A = final amount, P = principal, r = rate, n = frequency, t = time
  
  // Copilot will suggest implementation based on this context
}

// Generate sorting algorithm with performance optimisation
// Create a quick sort implementation that handles edge cases
// and provides O(n log n) average case performance

# Copilot CLI usage examples
gh copilot explain "git rebase -i HEAD~3"
gh copilot suggest "create a GitHub Actions workflow for Node.js testing"

# Complex chat prompts for development
# "Generate a React component with TypeScript that implements 
# a data table with sorting, filtering, and pagination. 
# Include proper error handling and accessibility features."

# Effective prompt patterns for code generation

# Pattern 1: Context + Intent + Constraints
"""
Context: Building a REST API for e-commerce platform
Intent: Create user authentication middleware
Constraints: 
- Use JWT tokens
- Include rate limiting
- Handle refresh tokens
- Implement proper error responses
- Follow security best practices
"""

# Pattern 2: Step-by-step breakdown
"""
Create a database migration system that:
1. Reads migration files from a directory
2. Tracks applied migrations in metadata table
3. Supports rollback functionality
4. Includes transaction safety
5. Provides detailed logging
"""

# Pattern 3: Example-driven prompting
"""
Following this pattern for API endpoints:
GET /api/users -> UserController.getUsers()
POST /api/users -> UserController.createUser()

Create similar endpoints for orders with:
- CRUD operations
- Pagination support
- Search functionality
- Input validation
"""

# Enterprise Copilot policy configuration
copilot_enterprise_policy:
  suggestions:
    enabled: true
    public_code_matching: "blocked"
    
  chat:
    enabled: true
    enterprise_knowledge_base: true
    
  data_handling:
    transmission_to_github: false
    retention_period: "zero_retention"
    
  content_exclusions:
    file_patterns:
      - "*.env"
      - "*.key"
      - "*.pem"
      - "**/secrets/**"
    
  compliance:
    audit_logging: enabled
    usage_monitoring: enabled
    privacy_mode: strict

// AI-assisted development workflow example
class AIEnhancedDevelopmentWorkflow {
  private copilot: GitHubCopilot;
  
  async generateFeature(requirements: string): Promise<FeatureImplementation> {
    // Use AI to break down requirements into tasks
    const tasks = await this.copilot.chat(`
      Break down these requirements into development tasks:
      ${requirements}
      
      Format as JSON with: description, complexity, dependencies, testing_requirements
    `);
    
    // Generate implementation with AI assistance
    const implementation = await this.generateImplementation(tasks);
    
    // AI-assisted code review
    const reviewSuggestions = await this.performAICodeReview(implementation);
    
    // Generate tests with AI
    const tests = await this.generateComprehensiveTests(implementation);
    
    return {
      implementation,
      tests,
      reviewSuggestions,
      documentation: await this.generateDocumentation(implementation)
    };
  }
  
  async generateComprehensiveTests(code: string): Promise<TestSuite> {
    return await this.copilot.chat(`
      Generate comprehensive test suite for this code:
      ${code}
      
      Include:
      - Unit tests for all functions
      - Integration tests for workflows
      - Edge cases and error scenarios
      - Performance benchmarks
      - Security vulnerability tests
    `);
  }
}

# AI-powered testing framework
class AITestingFramework:
    def __init__(self, copilot_client):
        self.copilot = copilot_client
        
    async def generate_test_suite(self, source_code, test_requirements):
        """Generate comprehensive test suite using AI"""
        prompt = f"""
        Generate a comprehensive test suite for this code:
        
        {source_code}
        
        Requirements:
        - {test_requirements}
        - Include positive and negative test cases
        - Cover edge cases and boundary conditions
        - Include performance and security tests
        - Generate test data and mocks
        - Follow pytest best practices
        """
        
        return await self.copilot.generate_code(prompt)
    
    async def identify_edge_cases(self, function_signature, business_logic):
        """Use AI to identify potential edge cases"""
        prompt = f"""
        Analyze this function and identify potential edge cases:
        
        Function: {function_signature}
        Business Logic: {business_logic}
        
        Consider:
        - Input validation scenarios
        - Boundary conditions
        - Error states
        - Performance edge cases
        - Security vulnerabilities
        """
        
        return await self.copilot.analyze(prompt)

{
  "copilot_privacy_settings": {
    "suggestions_enabled": true,
    "public_code_suggestions": "disabled",
    "telemetry_collection": "minimal",
    
    "content_exclusions": {
      "paths": [
        "src/secrets/",
        "config/production/",
        "*.env*",
        "**/*.key",
        "**/certificates/**"
      ],
      "file_types": [".pem", ".p12", ".jks"],
      "patterns": [
        "password.*=",
        "secret.*=",
        "api[_-]?key.*=",
        "token.*="
      ]
    },
    
    "enterprise_features": {
      "audit_logging": true,
      "usage_analytics": true,
      "content_scanning": true,
      "policy_enforcement": "strict"
    }
  }
}

# Enterprise AI governance framework
class CopilotGovernanceFramework:
    def __init__(self):
        self.policies = self.load_enterprise_policies()
        self.monitoring = UsageMonitoringService()
        
    def enforce_usage_policies(self, user_request):
        """Enforce enterprise AI usage policies"""
        # Check compliance requirements
        compliance_check = self.validate_compliance(user_request)
        
        # Verify data classification
        data_classification = self.classify_request_data(user_request)
        
        # Apply appropriate restrictions
        restrictions = self.apply_policy_restrictions(
            user_request, 
            compliance_check, 
            data_classification
        )
        
        return {
            'approved': restrictions.get('approved', False),
            'conditions': restrictions.get('conditions', []),
            'audit_trail': self.create_audit_record(user_request, restrictions)
        }
    
    def monitor_ai_usage(self):
        """Monitor and analyze AI usage patterns"""
        usage_metrics = {
            'total_requests': self.monitoring.get_request_count(),
            'code_generation_requests': self.monitoring.get_code_requests(),
            'chat_interactions': self.monitoring.get_chat_interactions(),
            'policy_violations': self.monitoring.get_violations(),
            'productivity_metrics': self.calculate_productivity_impact()
        }
        
        return self.generate_governance_report(usage_metrics)

// Complete AI development workflow
import { GitHubCopilot } from '@github/copilot-sdk';

class AIFeatureDevelopmentPipeline {
  private copilot: GitHubCopilot;
  
  constructor() {
    this.copilot = new GitHubCopilot({
      apiKey: process.env.COPILOT_API_KEY,
      organizationId: process.env.GITHUB_ORG_ID
    });
  }
  
  async developFeature(requirements: FeatureRequirements): Promise<FeatureDeliverable> {
    // Step 1: AI-assisted requirements analysis
    const analysis = await this.analyzeRequirements(requirements);
    
    // Step 2: Generate architecture with AI
    const architecture = await this.generateArchitecture(analysis);
    
    // Step 3: AI-powered implementation
    const implementation = await this.generateImplementation(architecture);
    
    // Step 4: AI-generated comprehensive tests
    const testSuite = await this.generateTestSuite(implementation);
    
    // Step 5: AI-assisted documentation
    const documentation = await this.generateDocumentation(implementation);
    
    // Step 6: AI code review and optimization
    const optimizations = await this.performAICodeReview(implementation);
    
    return {
      code: implementation,
      tests: testSuite,
      documentation,
      optimizations,
      deploymentGuide: await this.generateDeploymentGuide(implementation)
    };
  }
  
  private async analyzeRequirements(requirements: FeatureRequirements): Promise<RequirementsAnalysis> {
    const prompt = `
      Analyze these feature requirements and provide detailed breakdown:
      
      Requirements: ${requirements.description}
      Acceptance Criteria: ${requirements.acceptanceCriteria}
      Technical Constraints: ${requirements.constraints}
      
      Provide:
      1. Functional decomposition
      2. Technical architecture recommendations
      3. Risk assessment
      4. Implementation complexity estimation
      5. Testing strategy recommendations
    `;
    
    return await this.copilot.chat(prompt);
  }
}

# Advanced prompt engineering framework
class AdvancedPromptEngineer:
    def __init__(self):
        self.prompt_patterns = self.load_prompt_patterns()
        self.context_strategies = self.load_context_strategies()
    
    def engineer_code_generation_prompt(self, task, context, constraints):
        """Engineer optimized prompts for code generation"""
        base_prompt = self.build_base_prompt(task)
        
        # Add contextual information
        context_enhanced = self.enhance_with_context(base_prompt, context)
        
        # Apply constraints and requirements
        constrained_prompt = self.apply_constraints(context_enhanced, constraints)
        
        # Optimize for AI model understanding
        optimized_prompt = self.optimize_for_model(constrained_prompt)
        
        return {
            'prompt': optimized_prompt,
            'expected_tokens': self.estimate_token_usage(optimized_prompt),
            'quality_score': self.evaluate_prompt_quality(optimized_prompt)
        }
    
    def build_chain_of_thought_prompt(self, problem, examples=None):
        """Build chain-of-thought prompts for complex problems"""
        prompt_template = """
        Problem: {problem}
        
        Let's solve this step by step:
        
        1. Understanding the problem:
           - What are we trying to achieve?
           - What are the key components?
           - What are the constraints?
        
        2. Planning the solution:
           - What approach should we take?
           - What are the major steps?
           - What potential issues might we encounter?
        
        3. Implementation strategy:
           - How should we structure the code?
           - What patterns or libraries should we use?
           - How can we ensure quality and maintainability?
        
        4. Testing and validation:
           - How can we verify the solution works?
           - What edge cases should we consider?
           - How can we measure success?
        
        {examples_section}
        
        Now, provide the complete solution with detailed explanation.
        """
        
        examples_section = ""
        if examples:
            examples_section = f"""
            Examples for reference:
            {self.format_examples(examples)}
            """
        
        return prompt_template.format(
            problem=problem,
            examples_section=examples_section
        )

# Enterprise AI governance configuration
apiVersion: v1
kind: ConfigMap
metadata:
  name: copilot-enterprise-governance
data:
  governance-policy.yaml: |
    enterprise_ai_policy:
      version: "1.0"
      effective_date: "2025-11-01"
      
      access_controls:
        user_tiers:
          - name: "developer"
            permissions:
              - code_completion
              - chat_assistance
              - documentation_generation
            restrictions:
              - no_sensitive_data_processing
              - limited_enterprise_features
              
          - name: "senior_developer"
            permissions:
              - all_developer_permissions
              - advanced_prompt_engineering
              - ai_code_review
            restrictions:
              - audit_trail_required
              
          - name: "architect"
            permissions:
              - all_permissions
              - policy_configuration
              - usage_analytics
            restrictions:
              - full_audit_logging
      
      data_protection:
        classification_levels:
          - public: "no_restrictions"
          - internal: "basic_filtering"
          - confidential: "strict_exclusion"
          - restricted: "complete_prohibition"
        
        content_filtering:
          patterns:
            - "password.*="
            - "secret.*="
            - "api[_-]?key.*="
            - "private[_-]?key.*="
          
          file_exclusions:
            - "*.env*"
            - "*.key"
            - "*.pem"
            - "**/secrets/**"
            - "**/config/production/**"
      
      compliance_requirements:
        audit_logging:
          enabled: true
          retention_period: "7_years"
          detail_level: "comprehensive"
        
        usage_monitoring:
          real_time_alerts: true
          policy_violation_reporting: true
          performance_analytics: true
        
        data_sovereignty:
          geographic_restrictions: ["EU", "US"]
          data_residency_requirements: true
          cross_border_data_transfer: "prohibited"

# Enterprise AI governance implementation
from typing import Dict, List, Optional
from dataclasses import dataclass
from enum import Enum
import logging

class DataClassification(Enum):
    PUBLIC = "public"
    INTERNAL = "internal" 
    CONFIDENTIAL = "confidential"
    RESTRICTED = "restricted"

@dataclass
class GovernanceDecision:
    approved: bool
    restrictions: List[str]
    audit_required: bool
    reasoning: str

class EnterpriseAIGovernance:
    def __init__(self, policy_config: Dict):
        self.policy = policy_config
        self.audit_logger = logging.getLogger('ai_governance_audit')
        self.usage_monitor = UsageMonitoringService()
        
    async def evaluate_ai_request(self, request: AIRequest) -> GovernanceDecision:
        """Evaluate AI request against enterprise governance policies"""
        
        # Classify data sensitivity
        data_classification = await self.classify_request_data(request)
        
        # Check user permissions
        user_permissions = await self.get_user_permissions(request.user_id)
        
        # Evaluate content for policy violations
        content_analysis = await self.analyze_content(request.prompt)
        
        # Apply governance rules
        decision = self.apply_governance_rules(
            data_classification,
            user_permissions,
            content_analysis
        )
        
        # Log decision for audit trail
        await self.log_governance_decision(request, decision)
        
        return decision
    
    async def classify_request_data(self, request: AIRequest) -> DataClassification:
        """Classify the sensitivity level of the request data"""
        content = request.prompt + " " + request.context
        
        # Check for restricted patterns
        if self.contains_restricted_patterns(content):
            return DataClassification.RESTRICTED
            
        # Check for confidential indicators
        if self.contains_confidential_indicators(content):
            return DataClassification.CONFIDENTIAL
            
        # Check for internal-only content
        if self.contains_internal_indicators(content):
            return DataClassification.INTERNAL
            
        return DataClassification.PUBLIC
    
    def apply_governance_rules(self, 
                             classification: DataClassification,
                             permissions: UserPermissions,
                             content_analysis: ContentAnalysis) -> GovernanceDecision:
        """Apply enterprise governance rules to make access decision"""
        
        restrictions = []
        approved = True
        audit_required = False
        reasoning = []
        
        # Check data classification restrictions
        if classification == DataClassification.RESTRICTED:
            approved = False
            reasoning.append("Request contains restricted data")
            
        elif classification == DataClassification.CONFIDENTIAL:
            if not permissions.can_access_confidential:
                approved = False
                reasoning.append("User lacks confidential data access")
            else:
                restrictions.append("confidential_data_handling")
                audit_required = True
        
        # Check content violations
        if content_analysis.policy_violations:
            approved = False
            reasoning.extend(content_analysis.policy_violations)
        
        # Apply additional restrictions based on user tier
        if permissions.tier == "developer":
            restrictions.extend([
                "basic_features_only",
                "no_enterprise_data_access"
            ])
        
        return GovernanceDecision(
            approved=approved,
            restrictions=restrictions,
            audit_required=audit_required,
            reasoning="; ".join(reasoning)
        )

// Advanced prompt engineering and optimization system
interface PromptEngineering {
  pattern: string;
  context: ContextStrategy;
  optimization: OptimizationTechnique;
  evaluation: EvaluationMetrics;
}

class AdvancedPromptEngineeringSystem {
  private promptPatterns: Map<string, PromptPattern>;
  private contextStrategies: ContextStrategy[];
  private evaluationMetrics: EvaluationFramework;
  
  constructor() {
    this.initializePromptPatterns();
    this.setupEvaluationFramework();
  }
  
  async engineerOptimalPrompt(task: DevelopmentTask): Promise<OptimizedPrompt> {
    // Analyze task requirements
    const taskAnalysis = await this.analyzeTask(task);
    
    // Select appropriate prompt pattern
    const pattern = this.selectPromptPattern(taskAnalysis);
    
    // Build context-rich prompt
    const contextualPrompt = await this.buildContextualPrompt(pattern, task);
    
    // Optimize for AI model performance
    const optimizedPrompt = await this.optimizeForModel(contextualPrompt);
    
    // Evaluate prompt quality
    const qualityScore = await this.evaluatePromptQuality(optimizedPrompt);
    
    return {
      prompt: optimizedPrompt,
      pattern: pattern.name,
      qualityScore,
      estimatedTokens: this.estimateTokenUsage(optimizedPrompt),
      expectedOutput: this.predictOutput(optimizedPrompt)
    };
  }
  
  private async buildContextualPrompt(pattern: PromptPattern, task: DevelopmentTask): Promise<string> {
    const contextBuilder = new ContextBuilder();
    
    // Add technical context
    contextBuilder.addTechnicalContext({
      language: task.programmingLanguage,
      framework: task.framework,
      architecture: task.architecturalPattern,
      constraints: task.technicalConstraints
    });
    
    // Add business context
    contextBuilder.addBusinessContext({
      domain: task.businessDomain,
      requirements: task.functionalRequirements,
      goals: task.businessGoals
    });
    
    // Add quality context
    contextBuilder.addQualityContext({
      performanceRequirements: task.performanceTargets,
      securityRequirements: task.securityConstraints,
      maintainabilityGoals: task.maintainabilityTargets
    });
    
    return pattern.template.render(contextBuilder.build());
  }
}

// Prompt pattern library
const PROMPT_PATTERNS = {
  CHAIN_OF_THOUGHT: {
    name: "Chain of Thought",
    template: `
      Task: {task_description}
      
      Let's approach this systematically:
      
      1. Problem Analysis:
         - What exactly needs to be accomplished?
         - What are the key challenges?
         - What constraints must we consider?
      
      2. Solution Design:
         - What approach will work best?
         - How should we structure the solution?
         - What patterns or best practices apply?
      
      3. Implementation Plan:
         - What are the key components?
         - How will they interact?
         - What libraries or frameworks should we use?
      
      4. Quality Assurance:
         - How will we ensure correctness?
         - What tests are needed?
         - How will we handle edge cases?
      
      Context: {technical_context}
      Requirements: {requirements}
      Constraints: {constraints}
      
      Please provide a complete, well-documented solution.
    `,
    useCases: ["complex_algorithms", "system_design", "architecture_decisions"]
  },
  
  EXAMPLE_DRIVEN: {
    name: "Example-Driven Development", 
    template: `
      Create {target_description} following these patterns:
      
      Example 1:
      {example_1}
      
      Example 2:
      {example_2}
      
      Key patterns to follow:
      - {pattern_1}
      - {pattern_2}
      - {pattern_3}
      
      Now create: {specific_requirements}
      
      Ensure the solution:
      - Follows the established patterns
      - Maintains consistency with examples
      - Includes proper error handling
      - Has comprehensive documentation
    `,
    useCases: ["api_development", "component_libraries", "design_systems"]
  },
  
  CONSTRAINT_DRIVEN: {
    name: "Constraint-Driven Development",
    template: `
      Develop {solution_description} with these specific constraints:
      
      Technical Constraints:
      {technical_constraints}
      
      Performance Constraints:
      {performance_constraints}
      
      Security Constraints:
      {security_constraints}
      
      Business Constraints:
      {business_constraints}
      
      The solution must:
      1. Satisfy all constraints explicitly
      2. Provide reasoning for design decisions
      3. Include fallback strategies for constraint violations
      4. Demonstrate constraint validation
      
      Context: {context}
    `,
    useCases: ["enterprise_systems", "regulated_industries", "performance_critical"]
  }
};

# Enterprise AI development policy
ai_development_policy:
  principles:
    - human_oversight_required
    - transparency_in_ai_decisions
    - privacy_by_design
    - security_first_approach
    - continuous_monitoring
    
  usage_guidelines:
    approved_use_cases:
      - code_completion_assistance
      - documentation_generation
      - test_case_creation
      - code_review_assistance
      - learning_and_skill_development
      
    restricted_use_cases:
      - autonomous_deployment_decisions
      - security_critical_code_generation
      - personal_data_processing
      - regulatory_compliance_decisions
      
  quality_standards:
    code_review: "ai_generated_code_requires_human_review"
    testing: "comprehensive_testing_mandatory"
    documentation: "ai_rationale_must_be_documented"
    monitoring: "usage_patterns_continuously_monitored"

# Enterprise prompt engineering standards
class EnterprisePromptStandards:
    PROMPT_TEMPLATES = {
        'code_generation': {
            'structure': [
                'context_establishment',
                'requirements_specification', 
                'constraint_definition',
                'quality_criteria',
                'output_format'
            ],
            'required_elements': [
                'programming_language',
                'framework_specification',
                'error_handling_requirements',
                'testing_expectations'
            ]
        },
        
        'code_review': {
            'focus_areas': [
                'security_vulnerabilities',
                'performance_optimizations',
                'maintainability_improvements',
                'best_practices_compliance'
            ],
            'output_format': 'structured_feedback_with_priorities'
        },
        
        'documentation': {
            'requirements': [
                'api_documentation_standards',
                'code_comment_guidelines',
                'architecture_decision_records',
                'user_guide_specifications'
            ]
        }
    }
    
    @staticmethod
    def validate_prompt(prompt: str, template_type: str) -> ValidationResult:
        """Validate prompt against enterprise standards"""
        template = EnterprisePromptStandards.PROMPT_TEMPLATES[template_type]
        
        validation_results = []
        
        # Check required elements
        for element in template['required_elements']:
            if not EnterprisePromptStandards._contains_element(prompt, element):
                validation_results.append(f"Missing required element: {element}")
        
        # Check structure compliance
        structure_score = EnterprisePromptStandards._evaluate_structure(
            prompt, template['structure']
        )
        
        return ValidationResult(
            is_valid=len(validation_results) == 0 and structure_score > 0.8,
            issues=validation_results,
            quality_score=structure_score,
            recommendations=EnterprisePromptStandards._generate_recommendations(
                prompt, template
            )
        )

# AI productivity optimization and measurement
class AIProductivityOptimizer:
    def __init__(self):
        self.metrics_collector = ProductivityMetricsCollector()
        self.optimization_engine = OptimizationEngine()
        
    async def measure_ai_impact(self, team_id: str, time_period: str) -> ProductivityReport:
        """Measure AI impact on development productivity"""
        
        baseline_metrics = await self.get_baseline_metrics(team_id, time_period)
        ai_enhanced_metrics = await self.get_ai_enhanced_metrics(team_id, time_period)
        
        productivity_gains = self.calculate_productivity_gains(
            baseline_metrics, ai_enhanced_metrics
        )
        
        return ProductivityReport(
            code_generation_speed=productivity_gains.get('code_generation', 0),
            bug_reduction_rate=productivity_gains.get('bug_reduction', 0),
            test_coverage_improvement=productivity_gains.get('test_coverage', 0),
            documentation_quality=productivity_gains.get('documentation', 0),
            learning_acceleration=productivity_gains.get('skill_development', 0),
            overall_satisfaction=productivity_gains.get('developer_satisfaction', 0)
        )
    
    async def optimize_ai_workflows(self, team_metrics: ProductivityMetrics) -> OptimizationPlan:
        """Generate optimization plan for AI-enhanced workflows"""
        
        bottlenecks = self.identify_productivity_bottlenecks(team_metrics)
        opportunities = self.identify_optimization_opportunities(team_metrics)
        
        optimization_plan = OptimizationPlan()
        
        for bottleneck in bottlenecks:
            optimization_plan.add_recommendation(
                await self.generate_optimization_recommendation(bottleneck)
            )
        
        for opportunity in opportunities:
            optimization_plan.add_enhancement(
                await self.generate_enhancement_recommendation(opportunity)
            )
        
        return optimization_plan