QED: AI Development Patterns

A practitioner's knowledge base for AI-assisted development, organized by risk profile and context.

🎯 Navigate by Your Needs

By Risk Level

• 🟢 Low Risk Patterns - Start here for safe adoption
• 🟡 Managed Risk Patterns - Requires safeguards
• 🔴 High Risk Patterns - Critical systems only

By Context

• 🚀 Startup - High agility, low process
• 📈 Mid-Market - Scaling patterns
• 🏢 Enterprise - Governance focus
• 🏛️ Regulated - Compliance-first

By Domain

• Architecture - System design patterns
• Implementation - Development patterns
• Operations - Deployment and maintenance
• Security - Access control and compliance
• Team - Collaboration and process
• Quality - Validation and testing

📚 Learning Paths

• 🎓 Getting Started - 4-week structured program
• 🏢 Enterprise Adoption - Organizational rollout
• 🏗️ Agency Playbook - Client project patterns

🔍 Pattern Discovery

Multi-Dimensional Classification

Each pattern is tagged with:

• Domain: What problem area it addresses
• Risk Profile: Safety level (Green/Yellow/Red)
• Maturity: Experimental → Validated → Standard
• Context: Where it works best
• Relationships: Dependencies and conflicts

Pattern Template

All patterns follow a standard template including:

• Executive summary with risk assessment
• Implementation guide with prerequisites
• Trade-offs and failure modes
• Validation criteria and metrics
• Real-world case studies

📊 Knowledge Management Tiers

Tier 1: Research Collection

Raw articles and resources being evaluated

Tier 2: Critical Analysis

Patterns under professional evaluation with risk assessment

Tier 3: Proven Practice

QED Standard: Only patterns validated in production with documented outcomes

🎯 Who This Is For

• Individual Developers: Start with Getting Started
• Team Leads: Focus on Team Patterns and risk management
• Consultants: Use Context Guides for client-specific approaches
• Agencies: See Agency Playbook
• Enterprise Architects: Review High-Risk Patterns

⚖️ Core Principles

1. Risk-First Navigation: Traffic light system for immediate risk recognition
2. Evidence-Based: Only patterns proven in production environments
3. Context-Aware: Multiple paths based on your specific situation
4. Relationship Mapping: Understand dependencies before implementation
5. Evolutionary Tracking: Patterns mature as they're validated

🚀 Quick Start

For Safe Experimentation

1. Browse Low-Risk Patterns
2. Pick one pattern matching your context
3. Follow the implementation guide
4. Measure results
5. Share learnings

For Production Systems

1. Complete Risk Assessment
2. Review patterns for your Context
3. Check all Dependencies
4. Implement with safeguards
5. Monitor continuously

About QED

QED ("Quod Erat Demonstrandum" - "that which is demonstrated") follows a rigorous evidence-based approach. Every recommendation is backed by documented client project outcomes, with explicit discussion of trade-offs, limitations, and failure modes.

This guide reveals proven patterns from real production environments, including deep analysis of successful implementations with tools like Claude Code, Cursor, and enterprise AI-assisted development workflows.

About the Author

Hi! I'm Stephen Szermer, Chief Technology Officer with 15+ years of enterprise technology experience specializing in AI-assisted development and digital transformation. My background includes:

• AI/ML Leadership - Co-founder & CTO of Stage Zero Health, building AI-native platforms orchestrating 25+ clinical models
• Enterprise Digital Transformation - Driving 10-20% EBITDA improvements through strategic technology initiatives
• AI Development Patterns - Founder of PrivateLanguage.ai and creator of advanced AI workflow automation systems
• Regulated Industries Expertise - Implementing AI solutions in healthcare and financial services with strict compliance requirements

I'm passionate about transforming traditional industries by creating digital systems that amplify human expertise rather than replacing it.

Support This Work

I'm actively consulting on AI-assisted development for professional environments. If you need help with:

• AI coding assistant integration for client projects
• Risk assessment frameworks for AI tool adoption
• Digital transformation initiatives with AI components
• Professional AI workflow implementation with compliance requirements

Reach out by email or connect on LinkedIn.

Learn more about my consulting approach at StephenKeith.com.

Ready to Start?

Choose your path based on your needs:

New to AI development? → Start with Getting Started Guide

Need safe patterns? → Browse Low-Risk Patterns

Building for enterprise? → Review Enterprise Context

Want the full taxonomy? → Explore the Taxonomy Guide

Let's implement AI patterns that actually work in professional environments.