Developers Shift-Left Checklist

Purpose: Transform from traditional code writers → AI-co-creators who design prompts, validate AI outputs, and integrate AI capabilities with traditional software engineering rigor.

Pre-Development Setup (Both AI & Legacy)

AI Tool Configuration

• Set up AI development environment with preferred models (GPT-4, Claude, local models)
• Configure prompt libraries and templates with version control (Git-based prompt management)
• Establish AI usage guidelines including cost budgets, safety constraints, and output validation
• Create local development workflow for prompt testing and iteration
• Set up AI performance monitoring dashboards for cost, latency, and quality tracking

Code Analysis Infrastructure

• Deploy automated code analysis tools (SonarQube, CodeClimate, custom AI analyzers)
• Configure dependency scanning and security vulnerability detection
• Set up performance profiling and monitoring infrastructure
• Establish technical debt tracking with AI-generated metrics and prioritization
• Create living documentation system that updates automatically with code changes

AI-Specific Development Practices

Prompt Engineering as Code

• Create versioned prompt libraries with clear naming conventions and documentation
• Implement prompt testing frameworks with automated validation and performance benchmarks
• Design context engineering patterns for optimal AI model performance
• Establish A/B testing infrastructure for prompt optimization and model comparison
• Build prompt deployment pipelines with staged rollouts and rollback capabilities

AI Model Integration Architecture

• Design multi-model fallback strategies for reliability and cost optimization
• Implement response validation frameworks for safety, relevance, and quality checking
• Create intelligent caching systems for semantic similarity and exact match optimization
• Build batch processing capabilities for efficient AI model utilization
• Design rate limiting and circuit breaker patterns for AI service resilience

AI Safety and Monitoring

• Implement content moderation and safety filters for AI outputs
• Create bias detection and mitigation frameworks across different user demographics
• Design hallucination detection systems to identify and handle incorrect AI outputs
• Build privacy protection mechanisms to prevent data leakage in prompts and responses
• Establish continuous monitoring for model performance, cost, and safety metrics

Legacy-Specific Development Practices

Legacy System Analysis

• Use AI-powered codebase analysis to generate comprehensive system documentation
• Create automated dependency mapping with risk assessment and impact analysis
• Implement technical debt quantification using AI analysis and business impact modeling
• Generate security vulnerability reports with AI-suggested remediation strategies
• Build performance bottleneck identification with optimization recommendations

Legacy Integration Patterns

• Design strangler fig migration strategies with AI-generated implementation plans
• Create event-driven integration adapters between legacy and modern systems
• Implement API wrapper generation for legacy system modernization
• Build data synchronization mechanisms for gradual system migration
• Design rollback procedures and safety nets for all legacy integration changes

Legacy Modernization Framework

• Generate comprehensive test suites for existing functionality validation
• Create automated refactoring pipelines with AI-generated code improvements
• Implement database migration strategies with AI-assisted schema analysis
• Design performance optimization plans based on AI-generated bottleneck analysis
• Build monitoring and alerting systems for legacy system health and migration progress

Development Workflow Integration

AI-Assisted Development Process

• Integrate AI code generation into IDE workflow with validation and review processes
• Create automated code review systems that combine AI analysis with human oversight
• Implement AI-generated test case creation with coverage analysis and validation
• Build documentation generation pipelines that update automatically with code changes
• Design continuous learning systems that improve AI assistance based on developer feedback

Quality Assurance Integration

• Create AI-enhanced testing strategies combining generated tests with manual validation
• Implement automated security scanning with AI-generated vulnerability analysis
• Build performance regression detection using AI analysis of system behavior changes
• Design integration testing frameworks that validate AI and legacy system interactions
• Establish compliance checking systems for regulatory requirements (GDPR, HIPAA, etc.)

Continuous Improvement Practices

AI Model Management

• Monitor AI model performance metrics and establish retraining triggers
• Implement feedback collection systems to improve prompt quality and model selection
• Create cost optimization strategies based on usage patterns and performance requirements
• Build model version management with A/B testing and gradual rollout capabilities
• Design continuous learning pipelines for domain-specific AI capability improvement

Legacy System Evolution

• Track modernization progress metrics with business value assessment
• Implement automated regression testing for all legacy system changes
• Create knowledge transfer systems to document AI-generated insights about legacy systems
• Build technical debt reduction tracking with ROI analysis for refactoring investments
• Design team upskilling programs for AI-assisted legacy development techniques

Code Review and Validation

AI-Generated Code Review

• Validate AI-generated code for correctness, security, and performance before integration
• Review prompt quality and effectiveness, iterating for better outputs
• Test AI model behavior across different input scenarios and edge cases
• Verify integration points between AI-generated and human-written code
• Assess maintainability of AI-generated solutions and document decision rationale

Legacy Code Review

• Validate legacy analysis accuracy by comparing AI insights with system behavior
• Review migration strategies for completeness, safety, and business impact
• Test integration adapters thoroughly across all supported legacy system scenarios
• Verify performance optimizations don't break existing functionality
• Validate security improvements address identified vulnerabilities without introducing new risks

Deployment and Monitoring

AI System Deployment

• Deploy AI services with proper scaling, monitoring, and alerting configurations
• Monitor AI model performance in production with automatic degradation detection
• Track cost and usage metrics with budgeting and optimization recommendations
• Validate safety constraints in production with real user interactions
• Implement feedback loops for continuous model and prompt improvement

Legacy System Deployment

• Deploy legacy improvements using blue-green or canary deployment strategies
• Monitor system performance before, during, and after legacy system changes
• Validate business functionality through automated and manual testing procedures
• Track migration progress with rollback capabilities for all integration changes
• Document deployment outcomes and lessons learned for future legacy projects

Emergency Response Procedures

AI System Failures

• Implement fallback mechanisms when AI services become unavailable or degrade
• Create manual override procedures for critical AI-dependent functionality
• Establish escalation procedures for AI safety violations or inappropriate outputs
• Maintain backup AI providers for essential functionality during primary service outages
• Document incident response for AI-related failures and improvement opportunities

Legacy System Issues

• Prepare rollback procedures for all legacy system changes and integrations
• Maintain legacy system expertise through documentation and team knowledge sharing
• Create emergency support procedures for legacy system failures during migration
• Establish communication plans for stakeholders during legacy system incidents
• Document incident resolution and update prevention strategies for future development

Success Metrics and KPIs

AI Development Effectiveness

• Development velocity improvement through AI-assisted code generation and analysis
• Code quality metrics showing improvement in maintainability, security, and performance
• AI model performance meeting accuracy, latency, and cost targets in production
• Developer satisfaction with AI tools and integration into daily workflow
• Business value delivery through faster feature development and improved system capabilities

Legacy Modernization Progress

• Technical debt reduction measured through automated analysis and business impact
• System performance improvement in response time, throughput, and reliability metrics
• Security vulnerability resolution with measurable reduction in risk exposure
• Integration success rates for legacy system connections to modern platforms
• Knowledge transfer effectiveness enabling team independence in legacy system maintenance

Reminder: Developers are individual contributors who must validate all AI-generated outputs through rigorous testing, code review, and production monitoring. The prompt may be the new code, but developers remain responsible for system quality, security, and reliability.