Course Chapters
| Chapter | Title | Content |
|---|---|---|
| Prologue | The Rise of AI |
- Evolution from statistical machine learning to deep learning - The emergence and impact of transformer models - Key milestones in generative AI development Hands-on: Exploring the capabilities of different AI paradigms |
| Chapter 1 | Understanding LLMs |
- How Large Language Models are trained - Architecture and scaling principles - Data collection and training methodologies Hands-on: Experimenting with foundational GenAI tools (ChatGPT, Claude) |
| Chapter 2 | Working with LLMs |
- Token-based thinking and processing - Prompt engineering fundamentals - Techniques to optimize model performance Hands-on: Using structured prompting techniques to refine AI-generated content |
| Chapter 3 | Reasoning LLMs |
- Latest advancements in reasoning models (OpenAI's o-series, Deepseek r1) - Reinforcement learning for well-defined problems (math, coding) - Chain-of-thought and step-by-step reasoning Hands-on: Using thinking models to solve logical tasks compared to older models |
| Chapter 4 | Agents |
- LLMs within agentic frameworks - Tool use: web search, code interpreters, APIs - Retrieval-Augmented Generation (RAG) Hands-on: Building an AI-powered automation workflow using Zapier |
| Chapter 5 | Future with AI |
- Emerging trends and research frontiers - The evolving AI landscape - Preparing for future developments Hands-on: Teams create an AI solution integrating multiple tools from the course |