You picked up this book because your best prompt worked at noon and failed at 6, and you cannot explain why. You rewrote the instruction 3 times, got 3 different answers, and declared victory on the version that happened to pass. You may have built a RAG pipeline that finds text but misses meaning, or given Claude tools that it calls incorrectly, or watched a 40 turn conversation drift until your system prompt drowned. Whatever brought you here, you share a frustration: prompt engineering treats symptoms, not the system, and your production bill is 70% input context you never designed. This book is not a book of magic sentences. It is not a list of templates to copy, and it is not theory that ignores cost, latency, and failure. What you need is the discipline that top teams use, where the entire request is the product. You will learn to see the context window as a small architecture with a constitution, a toolkit, a conversation, and a library, and to engineer each region so the model behaves. This is context engineering, and it is the skill that separates occasional good results from reliable ones. Inside, you'll discover: . The anatomy of the context window: system prompt, tools array, messages, retrieved documents, and parameters . How to define good output before you ask, with audience, goal, content, format, and standard . Writing for a language model brain: roles, verbs, numbers, and tags that reduce guessing . Extending thinking when needed and keeping routine tasks on the fast path to save $ and latency . Structuring documents, summaries, and tables so the model treats facts as facts and instructions as instructions . Retrieval that respects context: contextual chunks, hybrid search with BM25, and top K budgeting . Tools as an extension of the prompt, memory as a design choice, and workflows before agents.