AI Learning Overview

Structured paths to develop practical AI skills without getting lost in the hype

Last updated: May 2025

Learning Philosophy

My approach to learning AI focuses on building practical capabilities that deliver immediate value while developing a foundation for long-term growth. Rather than trying to master everything at once, I've created focused learning paths that build complementary skills.

Each path is designed to take you from theory to practical implementation, with an emphasis on building real-world projects that demonstrate your capabilities.

The Three-Hat Framework

A key insight from my journey: successful AI implementation requires wearing three distinct hats:

  • 🏗️ Systems Architect:Think big picture, prevent technical debt, design for 10x scale
  • 📊 Product Manager:Optimize for user value, data-driven decisions, business impact
  • 💻 Implementation:Quality gates, developer experience, continuous improvement

Learning Paths

Technical Deep Dive

For those who want to understand the technical foundations of how LLMs work, from the mathematical foundations to implementation details.

Start Technical Path

Practical Implementation

Learn how to handle, deploy, and scale language models in production environments with real-world constraints and considerations.

Start Implementation Path

Claude Mastery

Master Claude's advanced capabilities including prompt engineering, tool use, computer use, vision, and RAG implementations.

Start Claude Path

Agent & RAG Systems

Build sophisticated AI agents and RAG systems that can interact with external tools, databases, and APIs to solve complex real-world problems.

Start Agent Path

General Resources

Beyond the structured learning paths, these resources provide valuable context and deeper understanding of AI concepts:

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