AI System Prompt Framework
Advanced prompt engineering framework highly encouraged and backed by VP of Engineering for engineering departments. Led AI enablement training with 8 company-wide AI 101 sessions plus 5 group/individual check-ins.
Enterprise AI Integration
IDE-Native Integration
- • Direct embedding within development environments
- • Context-aware code analysis and generation
- • Real-time standards enforcement during development
- • Seamless workflow integration without disruption
Multi-Language Support
- • C# with enterprise patterns and logging standards
- • C/C++ for embedded systems with safety constraints
- • JavaScript/TypeScript for web applications
- • Python, Java, SQL, Bash, Rust, and Go coverage
Compliance Engine
- • Automated standards enforcement with rule citations
- • Confidence-tagged output with uncertainty handling
- • Department-specific override and exception handling
- • Security and compliance violation prevention
Enterprise Features
- • Team-specific customization and override capabilities
- • Structured logging and observability integration
- • Security-first design with PII/credential protection
- • Comprehensive documentation and test generation
Technical Capabilities
Standards Enforcement
Automatically enforces coding standards with rule citations, ensuring consistent code quality across all teams and projects.
Confidence Tagging
Provides confidence levels for all suggestions with documentation references, enabling informed decision-making by developers.
Multi-IDE Integration
Seamlessly integrates with Cursor, Windsurf, and VsCode IDEs, providing consistent AI assistance across development environments.
Department Overrides
Supports team-specific rule customization while maintaining organization-wide consistency and compliance requirements.
Security Compliance
Built-in security patterns prevent unsafe code generation, credential exposure, and compliance violations.
Code Generation
Generates production-ready code with proper logging, documentation, testing patterns, and error handling.
AI-Generated Code Examples
Standards-Compliant C# Code
/// <summary>
/// Validates user input and processes order data
/// High confidence — Direct rule match (Standards 6.3.4)
/// </summary>
/// <param name="orderData">Order information to validate</param>
/// <returns>Processed order result</returns>
/// <exception cref="ArgumentException">Invalid order data</exception>
public async Task<OrderResult> ProcessOrderAsync(OrderData orderData)
{
if (null == orderData?.CustomerId) // Constants on left (Standards 6.2.3)
{
_logger.LogWarning("Invalid order data: {CorrelationId}",
correlationId); // Structured logging (Standards 4.2.3)
throw new ArgumentException("Order data cannot be null");
}
// Single return pattern (Standards 6.2.1)
return await _orderService.ProcessAsync(orderData);
}Confidence Annotation System
// Agent Output with Confidence Annotation:
// High confidence — Direct rule match (Standards 4.2.1)
// Added ILogger<T> injection for business logic compliance
// Medium confidence — Heuristic, aligned with project pattern
// Applied team-specific error handling approach
// Low confidence — No clear rule, suggest verifying with user
// Multiple approaches possible for this scenarioProject Achievements
Co-developed master IDE prompt highly encouraged by VP of Engineering
Backed by leadership as standard for engineering departments
Led 8 company-wide AI 101 training sessions
Conducted 5 additional group/individual AI check-ins
Mentored interns in prompt engineering and AI integration
Technical Implementation
Prompt Engineering Architecture
Developed sophisticated prompt system with role-based behavior definition, multi-language rule enforcement, and confidence-tagged output generation. The system maintains strict compliance with organizational standards while providing flexibility for team-specific requirements and department overrides.
Enterprise Compliance System
Built comprehensive compliance framework that prevents security violations, enforces documentation standards, and maintains audit trails. The system blocks unsafe code generation while providing clear guidance for compliant alternatives and rule citations for all suggestions.
Multi-Team Customization
Engineered flexible override system allowing department-specific customizations while maintaining organization-wide consistency. The system supports embedded systems constraints, cloud team rapid iteration needs, and mobile team platform-specific requirements within a unified framework.
Company-wide Implementation
Led comprehensive AI enablement initiative through training sessions and mentorship, establishing AI standards across engineering departments.
Enterprise Impact
Enhanced company's AI adoption, improving prompt engineering standards and developer workflows in engineering departments
Developer Productivity
- • Automated code standards enforcement reduces review cycles
- • Context-aware suggestions accelerate development
- • Consistent patterns across teams reduce onboarding time
- • Real-time guidance prevents technical debt accumulation
Organizational Benefits
- • Standardized AI practices across engineering departments
- • Reduced code review overhead through automated compliance
- • Enhanced security posture with built-in violation prevention
- • Company-wide AI training and mentorship program delivery
- • Growing adoption demonstrates measurable value delivery