After building 30+ repositories with AI coding tools, I've learned one thing: raw AI power without structure creates spectacular messes. Whether you're using ChatGPT, Cursor, or Claude Code, success depends on patterns—not prompts. I've been experimenting with these patterns for months, watching what works and what fails. The AI Development Patterns repository captures what I've learned, organized into three categories that actually solve real problems. Each pattern follows a standardized specification—this consistency ensures you can measure outcomes and adapt patterns reliably across projects. AI Development Patterns repository standardized specification The Pattern Framework That Keeps AI Focused Foundation Patterns create the groundwork. Start here—implement AI Readiness Assessment or Rules as Code today and see results in your first session. These patterns prevent the chaos before it starts. Development Patterns handle the heavy lifting. Specification Driven Development transformed how I build features. Instead of letting AI wander, I define what I want first. The AI implements within those boundaries. It's the difference between a helpful assistant and a rogue developer. Operations Patterns keep production systems running. Policy-as-Code Generation and Security Scanning Orchestration might not grab headlines, but they can prevent 3 a.m. wake-up calls. The real value emerges when patterns work together. Foundation enables development, development informs operations. It's a system, not a collection. Three Patterns You Can Use Today Specification Driven Development This pattern changed everything for me. I write annotated specs that clearly define the expected behavior, then ask AI to generate tests from those specs. Finally, AI implements the code to pass those tests. No more "close but not quite" implementations. When to use it: Any feature where the specs become the code - APIs, business logic, anything requiring precision. Why it works: AI excels at implementation but struggles with ambiguity. Clear specs = predictable results. Try it: Check the Specification Driven Development pattern and examples. Start with the simplest one. Specification Driven Development pattern examples AI-Driven Traceability AI maintains links between specifications, tests, and code automatically. When code changes, AI updates the connections. Nothing gets lost. The payoff: No more outdated documentation. No more orphaned code. Everything stays connected. Implementation: Add traceability to your workflow gradually. Start with commit messages, expand from there. See it work: The AI-Driven Traceability pattern and examples show multiple approaches for various project types. AI-Driven Traceability pattern examples AI-Driven Refactoring AI continuously analyzes code quality and suggests specific improvements. Not vague "make this better" suggestions—targeted refactoring with impact analysis. Best part: It fits perfectly into TDD's red-green-refactor cycle. Write the test (red), make it pass (green), then let AI analyze and suggest refactoring improvements. The AI respects your test boundaries while improving the implementation. How to start: Add refactoring analysis to your CI pipeline. After tests go green, AI suggests improvements. Review during planning or integrate into your TDD rhythm. Examples: The AI-Driven Refactoring pattern and examples show strategies for various codebases. AI-Driven Refactoring pattern examples What I've Learned About Implementation Start ridiculously small. Pick one pattern. Master it. Then add another. Teams that try everything at once struggle significantly. Constraints create quality. Unlimited AI creativity produces unlimited technical debt. Use specifications, tests, and clear boundaries. Channel the power, don't unleash it. Follow the progression. Simple prompts → test-driven AI → spec-driven development. Each stage builds on the previous. Integration matters. Patterns must fit your workflow. Custom commands, hooks, or CI/CD integration—make it seamless or it won't stick. The Repository: Your Starting Point The AI Development Patterns repository contains everything I've learned, structured for practical use. The standardized pattern specification means each pattern includes the same components: context, problem statement, solution approach, implementation guidelines, and measurable outcomes. This consistency matters—you can compare patterns, track success metrics, and know exactly what to expect. AI Development Patterns repository Browse by pain point: What's your biggest challenge right now? Code quality? Documentation? Testing? Start with the pattern that addresses your biggest problem. Use the examples: Don't theorize. Clone the examples, run them, modify them. Learning by doing beats reading every time. examples Check experimental patterns: The experimental section contains cutting-edge techniques. These are still being validated but show promising results. experimental section Your Next Move Pick one foundation pattern. I recommend Specification Driven Development if you're building features, or AI Readiness Assessment if you're just starting. Implement it on a small project. Something you can finish in a day. Experience the difference structure makes. Then expand. Add another pattern. Connect them. Build your own AI development system. We're in the early days of AI-native development. The patterns that seem revolutionary today will be standard practice tomorrow. By experimenting now and sharing what you learn, you're helping define how software gets built in the AI era. The repository is open source. Use it. Contribute to it. Help shape what comes next. Because here's what I know: AI isn't replacing developers. It's amplifying us. But only if we use it right. And that starts with patterns that actually work.

Want AI to Write Code That Doesn’t Break Everything? Follow These 3 Patterns

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