Acknowledgments
Preface
Introduction
Part I: Getting Started with AI
Interlude: The Startup Team Begins their AI Journey
Chapter 1: Introduction to Large Language Models
Chapter 2: Building Your First Application that Calls an LLM
Chapter 3: Python Essentials for Working with LLMs and APIs
Part II: Prompt Engineering
Interlude: Startup Growing Pains
Chapter 4: Fundamentals for Prompting LLMs
Chapter 5: Prompting Techniques for Creativity, Precision, and Control
Chapter 6: Libraries and Strategies for Prompting in Code
Part III: Vector Databases and RAG
Interlude: Product Refinement and Evolution
Chapter 7: Vector Databases in Practice
Chapter 8: Building a RAG Solution
Part IV: Fine-tuning
Interlude: The Inbox Problem
Chapter 9: Introduction to Fine-tuning
Chapter 10: Creating the dataset
Chapter 11: Getting Started with Fine-tuning
Chapter 12: Fine-tuning an LLM
Part V: Agents
Interlude: AI Steps into the Workflow
Chapter 13: Introduction to AI Agents
Chapter 14: Building Your First Agent
Chapter 15: Introduction to Tools
Chapter 16: Epilogue
The chapters in red are included in this Early Access PDF.