AI Engineering Fundamentals Workshop

📋 Syllabus

The approach is pragmatic: tools you can use in production today. We don't chase trends — we focus on patterns.

1. Foundations: APIs, LLMs & Testing

• ▸Model Agnosticism: How to use libraries and design patterns to switch between OpenAI, Anthropic, and Open Source without breaking your code.
• ▸Prompt Management: Professional organization of prompts (no more scattered strings throughout the code).
• ▸Trust & Evals: Introduction to Guardrails and OpenEvals. You'll learn to write tests for your AI, just like you write unit tests for your code.

2. RAG (Retrieval Augmented Generation)

• ▸Beyond "Hello World": Vector Stores, Embeddings, and Chunking strategies.
• ▸Advanced Extraction: How to connect your private data securely.
• ▸Hallucination Control: Techniques to make the model respond strictly based on your documentation.

3. Low-Code Automation

• ▸Visual automation: How to orchestrate complex flows to integrate with models and introduce complex business logic.
• ▸Real-World Connections: Integrations with Email, CRMs, and others to create immediate value flows.

4. The Future: Autonomous Agents

• ▸Agent Architecture: Understand what they really are and how they are designed using Google ADK.
• ▸Function Calling: How to allow an LLM to use external tools reliably and safely.