<2-3 sentences summarising what the project addresses and solves, the target audience, and the benefit.> This repository offers a structured framework of AI development patterns designed to guide teams in building software with AI assistance. It categorizes patterns by implementation maturity and lifecycle phases (Foundation, Development, Operations), providing practical examples and anti-patterns to foster efficient, secure, and scalable AI-augmented software engineering. The benefit is a systematic approach to adopting AI in development workflows, reducing common pitfalls and accelerating adoption.

How It Works

The project organizes AI development patterns into three main categories: Foundation (team readiness, basic integration), Development (daily coding workflows), and Operations (CI/CD, security, production management). Patterns are further classified by maturity (Beginner, Intermediate, Advanced) and can be implemented sequentially over phases or continuously. The core approach emphasizes a systematic, phased learning progression for teams new to AI development, while experienced teams can adopt patterns continuously.

Quick Start & Requirements

This repository provides a collection of patterns and does not have a direct installation command or executable tool. Requirements for implementing the patterns vary by maturity level, ranging from basic programming skills and AI tool access for Beginner patterns to more advanced prerequisites for Intermediate and Advanced patterns. Specific dependencies like GPU, CUDA, or Python versions are not mandated by the repository itself but would depend on the AI tools and environments used to implement the patterns.

Highlighted Details

• Phased Implementation: Patterns are organized into learning phases (Foundation, Development, Operations) with suggested timelines, but continuous implementation is recommended for security and deployment patterns.
• Maturity Levels: Patterns are classified as Beginner, Intermediate, or Advanced, indicating complexity and prerequisite knowledge.
• Task Sizing Framework: Provides distinct approaches (AI Issue Generation, Atomic Task Decomposition, Progressive AI Enhancement) tailored for different development contexts (human teams, parallel AI agents, rapid feedback).
• Decision Framework: A decision tree and context-based recommendations help teams select appropriate patterns based on their AI development maturity, project type, team size, and technology stack.

Maintenance & Community

The repository encourages community contributions via issues and pull requests, acknowledging the rapidly evolving nature of AI development. Specific details on maintainers, sponsorships, or dedicated community channels (like Discord/Slack) are not provided in the README.

Licensing & Compatibility

The project is released under the MIT License, which permits commercial use and closed-source linking with minimal restrictions, primarily requiring attribution.

Limitations & Caveats

This repository is a conceptual framework and collection of patterns, not a ready-to-use tool. Implementing these patterns requires significant effort in setting up AI tools, environments, and integrating them into existing workflows. The patterns are subject to change as the AI development field evolves.