A 25-session hands-on course to learn AI/LLM development, using the Anthropic Console for interactive experiments.
Last updated: February 2026 | Main model: Claude Opus 4.5
Each session is ~1 hour of hands-on learning. Claude Code serves as your instructor — guiding you through experiments, explaining concepts, and tracking progress.
Running example: You'll build an AI feature for YOUR product throughout the course, applying each concept to something real.
cd path/to/AIPM
claudeThen say:
- "Let's get started" - Run first-time setup (name, product, etc.)
- "Let's do Session 1" - Start a specific session
- "Continue my course" - Resume where you left off
- "Show my progress" - See completion status
| File | Purpose |
|---|---|
| README.md | Course overview (this file) |
| LESSONS.md | Detailed plans for all 25 sessions |
| CLAUDE.md | Instructor instructions |
| user.json | Your profile and product (created at setup) |
| progress.json | Your progress data |
| PROGRESS.md | Your progress with checkmarks |
| MY_PRODUCT.md | Your running example |
| workspace/.env | API keys |
- LLM Architecture & API Fundamentals
- Prompt Engineering Patterns
- Embeddings & Semantic Similarity
- Vector Databases Deep Dive
- Document Processing & Chunking
- Building Your First RAG Pipeline
- Function Calling & Tool Use
- ReAct Agents & Extended Thinking
- Memory Systems for Stateful Agents
- Advanced RAG Patterns
- Corrective & Autonomous RAG
- When RAG Fails — Agentic Search Alternatives
- Voice & Multimodal Agents
- Multi-Agent Architecture Patterns
- CrewAI for Role-Based Teams
- LangGraph for Complex Workflows
- OpenAI Agents SDK Deep Dive
- Google ADK for Enterprise Agents
- MCP & Standardized Tool Calling
- Evaluation Frameworks That Matter
- Safety, Guardrails & Responsible AI
- RLHF & Alignment Deep Dive
- System Design for AI Products
- Fine-tuning vs RAG vs Agentic Search Decision Framework
- Capstone Integration
- Access to console.anthropic.com
- An Anthropic API key (add to workspace/.env)