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Welcome to AI System Design

A comprehensive curriculum for designing effective AI systems through Specifications, Skills, and Advanced Prompting.

What This Documentation Covers

This documentation provides practical guidance for anyone working with AI systems—especially Large Language Models (LLMs). Whether you're an AI engineer, developer, product manager, or just getting started, you'll find actionable frameworks and real-world examples.

Core Topics

📋 Specifications
Learn how to create clear, effective specifications for AI systems. Master the framework of MUST constraints, SHOULD guidelines, CONTEXT, INTENT, and VERIFICATION protocols.

🛠️ Skills
Discover how to design reusable AI capabilities. From basic skill anatomy to advanced implementations, including tool design and semantic tagging.

🔧 Tools
Explore the tools and templates for building AI systems, including tool literacy and practical implementation guides.

🚀 Advanced Prompting
Master advanced prompting techniques, automated optimization, and building reliable AI agents.

🚀 Multi-Agent
Learn how multi-agent systems change the stakes, the cascade problem, how Specifications, Skills,
and Prompts extend to agent-to-agent interactions, and what stays the same.

Who This Is For

  • AI Engineers building production AI systems
  • Developers integrating LLMs into applications
  • Product Teams defining requirements for AI features
  • Technical Leaders establishing AI development standards
  • Beginners just starting their AI journey

No AI expertise required—this teaches from fundamentals to advanced concepts.

Getting Started

New to AI System Design?

Start with the Specifications section to understand how to define clear requirements, then move through Skills and Tools before tackling Advanced Prompting.

Recommended path:
Specifications → Skills → Tools → Advanced Prompting

Looking for Specific Information?

Use the search feature (top of page) or jump directly to:

What Makes This Different

Most AI documentation teaches isolated techniques: "here's how to prompt better" or "here's how to build a RAG system." This curriculum is different.

It unifies the field around one core principle: reducing cognitive friction for AI models.

Rather than collecting scattered tricks and workarounds, this documentation:

  • Synthesizes research and practice - Combines what researchers identify as best practices with what practitioners discover works (or fails) in real-world use
  • Provides the model's perspective - Written with insights from Claude's direct experience: what causes friction, what helps, and why
  • Teaches the complete system - Specifications, Skills, Tools, and Advanced Prompting working together as an integrated approach
  • Stress-tested across models - Stress-tested with Google Gemini after each module, confirming these principles reflect fundemental model behavior, not model-specific quirks.
  • Captures a moment in time - A January 2026 snapshot of rapidly evolving AI engineering practices

The result: You understand not just what to do, but why it works from the model's point of view—helping you build more reliable AI systems with greater certainty.

How to Use This Documentation

Read sequentially - Each section builds on previous concepts
Jump to topics - Use navigation or search for specific information
Try the examples - Apply concepts to your own projects
Reference appendices - Quick templates and guides when you need them

Contributing

Found an issue or want to contribute? Visit the GitHub repository to report bugs, suggest improvements, or submit pull requests.

About

This documentation was created through collaboration between Archie Cur and Claude (Anthropic), combining human vision and AI capability to create practical, actionable guidance for AI system design.

Ready to get started? Begin with Specifications →