At a Glance
Whether you want to build your first RAG pipeline, ship an AI agent to production, or make strategic decisions about LLM adoption at your organization, this book meets you where you are. It is for software engineers, ML practitioners, researchers, product leaders, domain specialists, and educators who want to understand, build, and deploy systems powered by large language models. It assumes familiarity with Python and basic linear algebra; appendices cover the remaining prerequisites.
The book spans 39 chapters (numbered 0 through 38) in 11 parts, plus 22 appendices (A through V) with framework tutorials, and a capstone project. For the full chapter map, dependency diagram, audience details, and background requirements, see FM.1: What This Book Covers. Twenty tailored reading pathways help you find the most relevant chapters for your goals.
How This Book Was Created
This book was produced through a collaborative process between its human authors and a team of 42 specialized AI writing agents. The authors curated every chapter, validated all technical content, and made all editorial decisions; AI agents proposed initial drafts, generated code examples, created illustrations, and checked cross-references across the 39-chapter structure.
Many of the book's illustrations were produced using Google Gemini's image generation capabilities, with prompts crafted by the authors and refined through iterative feedback. All diagrams and SVG figures were either hand-coded or generated and reviewed for technical accuracy.
The result is a textbook that combines the depth and rigor of expert-authored content with the breadth and consistency that AI-assisted production enables, covering a field that evolves too rapidly for traditional publishing timelines.
To meet the specialized agents, see The Writing Team. For the 42 fictional AI characters who contribute the opening epigraphs, see The Wisdom Council.