QED
Quod Erat Demonstrandum - “That Which Is Demonstrated”
AI Development Patterns: A Practitioner's Guide
Evidence-based patterns for AI-assisted development. Built from published research and analysis of production agent systems, with a risk assessment for each pattern.
Access the full guide with 30+ patterns, learning paths, and case studies
Builds on Gerred Dillon's Building an Agentic System. Licensed CC BY 4.0.
The Practitioner's Challenge
When you're responsible for delivering AI solutions to clients, every pattern recommendation carries professional liability. QED fills the gap between impressive demos and production-ready implementations that actually work in enterprise environments.
You're accountable for security decisions, architecture choices that scale, risk assessments that prevent failures, and performance guarantees that meet enterprise expectations.
🎯 Navigate by Risk Level
🏢 Navigate by Context
🔧 Navigate by Domain
The QED Methodology
Evidence-based pattern organization with systematic validation:
Comprehensive intake of industry patterns and frameworks
Professional evaluation with risk assessment matrices
Reserved for patterns validated in production with documented outcomes. None have been promoted yet.
What Makes QED Different
Every pattern traced to published sources or analysis of production systems
Systematic evaluation for enterprise architecture decisions
Patterns validated with accountability in mind
Security, privacy, and compliance considerations
Documented failure modes and known constraints
Specific metrics and validation criteria
Who This Is For
Delivering AI solutions to enterprise clients
Evaluating AI integration strategies
Building production AI architectures
Creating AI-powered client applications
Managing client AI projects
Designing compliant AI systems