How to Learn AI
A structured approach to developing AI expertise for different career paths
Learning Pathways
I've developed these learning pathways based on my own journey and extensive research into effective approaches for developing AI expertise. The pathways are designed to provide both breadth and depth, depending on your goals.
Broad LLM Handling
This pathway focuses on developing general skills for working with large language models across a variety of applications. It's ideal for professionals who need to incorporate AI capabilities into existing workflows or products.
- Prompt engineering fundamentals
- API integrations with popular models
- Fine-tuning for specific use cases
- Building augmented workflows
AI Agent & RAG Development
This advanced pathway focuses on building autonomous agents and retrieval-augmented generation (RAG) systems. It's designed for developers who want to create more sophisticated AI applications.
- Agent architecture design
- Vector database implementation
- Knowledge retrieval systems
- Multi-agent communication
Core Learning Principles
Learn by Building
The most effective way to learn AI is through practical application. Focus on building real projects that solve actual problems.
Understand the Fundamentals
While you don't need to know everything about how models work, having a solid conceptual understanding will make you more effective.
Iterative Improvement
Start simple, then progressively enhance your projects as you learn more. This iterative approach builds confidence and skills.