Introducing AI Development Patterns
Centralized, evolving resource of AI-driven coding patterns—contexts, solutions, trade-offs.

My recent post on ATDD-driven AI development sparked feedback from adopters and inquiries about applying it elsewhere. What stood out was how many builders share the same core questions: How do we code effectively with AI? Which practices deliver real results? Which pitfalls must we avoid?
We're not even sure what to call this new paradigm yet. Is it AI-Native Development? Vibe Coding? Coding with AI? AI-Assisted Development? Agentic Development? Agentic Coding? Something else entirely? The terminology is still evolving as rapidly as the practices themselves.
This feedback led to a realization: we're all pioneering this new frontier together, yet our learnings remain scattered across blog posts, tweets, and closed-door team retrospectives. I wanted a centralized, evolving resource that captures these emerging practices and thought others might benefit from it too.
Why Patterns?
Over the past several months, I've been documenting the practices that are working for me as I code with AI. These patterns emerge from my personal development experience and observations of how others are adapting to AI-assisted coding. Rather than keeping these as private notes, I decided to structure them as patterns—a time-tested format that clearly articulates:
- Context: When does this pattern apply?
- Problem: What challenge does it address?
- Solution: How do you implement it?
- Consequences: What are the trade-offs?
Equally important are the anti-patterns—those seemingly reasonable approaches that lead to frustration, technical debt, or outright failure. Learning what doesn't work is often as valuable as knowing what does.
Open Source from Day One
I've published these patterns in a public GitHub repository: https://github.com/PaulDuvall/ai-development-patterns
Why make it public while it's still evolving? Three reasons:
- Collective Intelligence: The AI development community is discovering what works in real-time. By sharing my experiences early, others can test these practices in their contexts and share what works (or doesn't) for them.
- Rapid Evolution: AI tools are advancing at breakneck speed. What works for me today might be obsolete tomorrow. A public repository allows us to iterate quickly and keep pace with the changing landscape.
- Diverse Perspectives: Every team has unique constraints and contexts. What's working in my environment might need adaptation elsewhere. Community feedback helps identify which practices have broader applicability.
Current Pattern Categories
The repository currently organizes patterns into three main categories:
Foundation Patterns
Essential patterns for team readiness and basic AI integration:
- AI Readiness Assessment: Evaluating if your codebase and team are prepared for AI adoption
- Rules as Code: Versioning AI coding standards like infrastructure
- AI Security Sandbox: Running AI tools without credential leak risks
Development Patterns
Daily practice patterns for AI-assisted coding workflows:
- Specification Driven Development: Using executable specs to guide AI code generation
- Progressive AI Enhancement: Building features incrementally rather than all at once
- Context Window Optimization: Matching AI tool selection to task complexity
Operations Patterns
CI/CD, security, and production management with AI:
- Pipeline Synthesis: Converting plain English to CI/CD configurations
- Security Scanning Orchestration: AI-powered security finding summarization
- Technical Debt Forecasting: Proactive AI-driven code quality management
What Makes This Different?
This isn't another "10 ChatGPT Prompts for Developers" listicle. These patterns represent:
- Working practices from my codebases and what I've seen from others
- Clear anti-patterns showing what hasn't worked for me and why
- Maturity levels (Beginner/Intermediate/Advanced) for adoption guidance
- Pattern relationships showing how practices build on each other
- Concrete examples with actual code and configurations I'm using
Join the Conversation
This repository represents my current understanding, but it's far from complete. I'm actively seeking feedback on:
- Which patterns resonate with your experience? Have you successfully applied similar practices?
- What's missing? What practices have you discovered that aren't captured here?
- What doesn't work? Have you tried any of these practices and found them lacking?
- Context matters: How do these practices need to adapt for your specific environment?
Looking Forward
We're in the early days of AI-assisted development. The patterns that seem revolutionary today might be table stakes tomorrow. By documenting and sharing our collective experiences, we can:
- Accelerate the learning curve for teams adopting AI
- Avoid repeating costly mistakes
- Establish common vocabulary and practices
- Build on each other's innovations
The goal isn't to create rigid rules but to capture flexible practices that teams can adapt to their contexts. As AI capabilities evolve, so too will these patterns.
Get Started
Visit https://github.com/PaulDuvall/ai-development-patterns to explore the current practices. Star the repository to stay updated, and consider contributing your own experiences.
What practices have you discovered in your AI development journey? I'd love to hear about your experiences and add them to this growing collection.