AI Engineering
Building Production-Ready LLM Applications
Preface
Welcome to AI Engineering: Building Production-Ready LLM Applications.
This book is designed for software engineers who want to build production-quality AI systems. Whether you’re adding LLM capabilities to an existing application or building AI-native products from scratch, this book provides the practical knowledge you need.
Who This Book Is For
- Software engineers transitioning into AI/ML roles
- Backend developers integrating LLMs into production systems
- Full-stack developers building AI-powered applications
- Tech leads architecting AI systems at scale
- ML engineers looking to strengthen their engineering fundamentals
How to Use This Book
The book is organized into five parts:
Part I: Foundations covers the essential background—Python proficiency, ML fundamentals, and building your first LLM application.
Part II: Core LLM Development dives deep into the technologies that power modern AI applications: transformer architectures, prompt engineering, RAG systems, and agentic patterns.
Part III: Production Engineering focuses on taking AI from prototype to production: deployment, backend integration, MLOps, security, and responsible AI practices.
Part IV: Professional Growth addresses the human side of AI engineering: deepening expertise, project ownership, and technical communication.
Part V: Staff+ Engineering covers advanced topics for senior engineers: system design at scale, performance optimization, cost engineering, and technical leadership.
Code Examples
All code examples are available in the companion repository. Each chapter includes runnable examples that you can experiment with immediately.
git clone https://github.com/jchu0/ai-engineering-textbook
cd ai-engineering-textbook/codeConventions Used
Throughout this book:
Code snippetsappear in monospace font- Key terms are bolded on first use
- Diagrams use consistent visual language across chapters
- Each chapter ends with key takeaways and exercises
Let’s begin.