Introduction

When you're responsible for delivering AI solutions to clients, every pattern recommendation carries professional liability. This is QED: AI Development Patterns - a practitioner's knowledge base built on evidence-based methodology and systematic risk assessment.

QED stands for "Quod Erat Demonstrandum" - that which is demonstrated. In mathematics, it marks the completion of a proof. In consulting practice, it represents patterns that have been tested in real client environments and proven to deliver measurable outcomes.

This guide emerged from a critical gap in AI development resources: the disconnect between impressive demos and production-ready implementations that actually work in enterprise environments. While the internet overflows with AI tutorials and framework evangelism, practitioners need systematic guidance for making architecture decisions that won't compromise client projects.

The Practitioner's Challenge

Building AI-powered systems for clients requires more than technical proficiency. You're accountable for:

Security decisions that protect client data and intellectual property
Architecture choices that scale with business requirements
Risk assessments that prevent costly implementation failures
Framework selections that maintain long-term viability
Performance guarantees that meet enterprise expectations

Traditional AI content rarely addresses these constraints. QED fills that gap with systematic evaluation frameworks and evidence-based pattern recommendations.

The QED Methodology: Evidence-Based Pattern Organization

QED employs a systematic approach to pattern validation that ensures every recommendation has been battle-tested:

Tier 1: Research Collection (docs/) - Comprehensive intake of industry patterns, frameworks, and case studies with systematic priority assessment based on client relevance.

Tier 2: Critical Analysis (src/analysis/) - Professional evaluation using risk assessment matrices, client context analysis, and implementation feasibility studies with structured evaluation frameworks.

Tier 3: Proven Practice (src/patterns/) - Only patterns that have been successfully deployed in client environments with documented outcomes, metrics, and lessons learned.

Taxonomy-Driven Organization

Patterns are organized across multiple dimensions to support different decision-making contexts:

By Domain - Technical implementation areas (Architecture, Implementation, Operations, Security, Team, Quality)

By Risk Profile - Assessment categories (Low Risk, Managed Risk, High Risk) based on implementation complexity and failure impact

By Context - Business environments (Startup, Mid-market, Enterprise, Regulated) with specific constraints and requirements

By Learning Path - Structured journeys for different practitioner needs (Getting Started, Enterprise Adoption, Agency Playbook, Traditional Migration)

This multidimensional approach ensures patterns can be discovered through the lens most relevant to your current decision-making context.

Domain-First Navigation (patterns/) - When you know what technical area you're working on:

Architecture - System design, component structure, integration patterns
Implementation - Coding approaches, framework selection, development workflows
Operations - Deployment, monitoring, performance optimization
Security - Authentication, permissions, data protection
Team - Collaboration patterns, enterprise integration, knowledge sharing
Quality - Testing strategies, risk assessment, validation approaches

Context-First Navigation (by-context/, by-risk/) - When your constraints drive decisions:

Startup - Resource-conscious patterns for rapid iteration
Enterprise - Governance-compliant patterns for scale
Regulated - Compliance-first patterns for sensitive industries
Low/Managed/High Risk - Patterns categorized by implementation complexity

Learning-First Navigation (learning-paths/) - When you need structured guidance:

Getting Started - Foundation patterns for AI development newcomers
Enterprise Adoption - Systematic rollout for large organizations
Agency Playbook - Client-focused patterns for consulting work
Traditional Migration - Moving from conventional to AI-augmented development

What Makes QED Different

Unlike typical AI development resources, QED provides:

Evidence-based recommendations with documented client outcomes
Risk assessment frameworks for enterprise architecture decisions
Client context considerations (security, privacy, compliance requirements)
Professional liability awareness in every pattern recommendation
Systematic evaluation methodology rather than framework evangelism
Transparent limitations - we document failure modes and known constraints

How to Use This Guide

Choose your entry point based on your current context:

New to AI Development?

Start with the Getting Started Learning Path which provides:

Foundation concepts and philosophy
Step-by-step pattern implementation
Progressive skill building over 4 weeks
Hands-on exercises with real projects

Have Specific Technical Questions?

Navigate by domain in the Patterns section to find:

Architecture patterns for system design
Implementation guides for specific frameworks
Security patterns for enterprise requirements
Team collaboration patterns for organizational adoption

Working Within Constraints?

Use context-driven navigation:

By Business Context - Find patterns for your organizational type
By Risk Profile - Match patterns to your risk tolerance
Migration Guidance - Transition from current to AI-augmented workflows

Each pattern includes:

Risk assessment with specific mitigation strategies
Context applicability matrix showing best-fit scenarios
Implementation roadmap with validation checkpoints
Trade-off analysis comparing alternatives
Real deployment outcomes with measurable results

Target Audience

QED serves practitioners who are accountable for AI implementation success:

Technical consultants delivering AI solutions to enterprise clients
CTOs and technical leaders evaluating AI integration strategies
Senior engineers responsible for production AI system architecture
Systems integrators building AI-powered client applications
Anyone who needs evidence-based guidance rather than framework marketing

Attribution and Sources

QED builds upon extensive research and analysis of production systems. Key foundational work includes:

Gerred Dillon's "Building an Agentic System" (gerred.github.io/building-an-agentic-system) provides exceptional technical analysis of real production systems including Claude Code and Amp. His systematic approach to analyzing implementation patterns forms a crucial foundation for QED's methodology.

Jason Liu's practitioner insights on context engineering, RAG systems, and agentic architectures contribute evidence-based patterns tested in real client environments.

All patterns in QED undergo systematic evaluation and client validation before inclusion. We maintain full attribution to original sources while adding our own analysis, risk assessment, and client deployment experience.

QED is licensed under Creative Commons Attribution 4.0 to encourage knowledge sharing while maintaining attribution to contributing practitioners.