AI Automation Mastery
Learn practical AI automation through 30 hands-on exercises.
This course teaches you to build AI-powered workflows using n8n and modern AI models. You’ll learn universal automation patterns that work across any industry or use case.
Who This Is For
- Non-technical professionals wanting to automate work without coding
- Technical people transitioning into AI and automation
- Students and alumni improving employability through automation skills
- Anyone seeking practical AI skills applicable to their job
Course Philosophy
Learn Universal Patterns, Not Just Tools
Instead of memorising specific buttons and features, you’ll learn automation patterns that apply everywhere:
- Dynamic Routing (Exercise 1): Break complex problems into simple, manageable pieces
- AI Agent Chaining (Exercise 2): Connect specialised AI tasks for powerful results
- Quality Control Loops (Exercise 3): Ensure AI output meets your standards automatically
Simplicity and Modularity
Why This Matters: We teach you to build workflows where each component does ONE job well. Need to change how urgent emails are handled? Swap out that one piece. The rest keeps working. This modularity means you can evolve systems without breaking them.
Course Structure
Current Exercises
Exercise 1: Email Classification & Routing (45 min)
Learn dynamic routing - automatically handle different situations differently. Uses OpenRouter to explore multiple AI model options.
- Data ingestion, classification, routing, logging
- Introduction to model aggregators (OpenRouter)
- Swappable components and modular design
Exercise 2: AI Cold Email Automation (60 min)
Chain multiple AI agents together. Uses Google Gemini for fast, reliable AI responses.
- AI Research Agent + Content Generation Agent
- Tool usage (web search for AI)
- Transitioning between model providers
Exercise 3: LLM as a Judge (60 min)
Build quality control systems with loops and exit conditions. Continues with Google Gemini for iteration speed.
- Self-improving AI systems
- Structured vs unstructured outputs
- Loop anatomy and infinite loop prevention
What You’ll Learn
- Break problems into smaller pieces (modularity and simplicity)
- Experiment with different AI models (OpenRouter, Google Gemini)
- Chain AI agents for complex tasks (research + generation + evaluation)
- Build quality control into automation (loops and exit conditions)
- Swap components easily (change one part without breaking others)
Getting Started
Technologies Covered
Prerequisites
- Computer with internet access
- Free accounts for required services (instructions provided)
- Basic understanding of APIs helpful but not required