Context Engineering

AI agents don’t fail because they forget everything. They fail because they remember badly. This book shows you how to engineer memory that stays accurate, efficient and useful over time. From SQLite and PostgreSQL to vector indexes and multi-agent systems, you’ll learn what it takes to build agents that can run for days without losing the plot.

AI can write code fast, but getting reliable software still starts with clear thinking. This book shows how to turn ideas into precise specifications, then use AI assistants and autonomous agents to build, test and maintain real-world software. Follow a complete project from business problem to production and learn a practical approach to AI-assisted development.

A vendor-neutral, mechanism-level field guide to operating and extending agentic coding harnesses (542 manuscript pages).

Turn a small language model into an AI agent that runs on your own hardware. This practical guide takes you from choosing a base model to fine-tuning, tool use, evaluation and deployment. With reproducible code and real configurations throughout, you'll learn how to build specialized local agents that are capable, efficient and truly yours.

Go beyond the basics of Claude Code with a practical engineering reference built for real-world development. Explore architecture, workflows, security, team adoption and advanced agentic patterns, with concrete commands and working examples throughout. Learn how to use Claude Code effectively, reliably and safely in professional software projects.

AI can sound certain even when it is completely wrong. This book takes a practical approach to building RAG systems and AI agents that ground answers in evidence, verify what they generate and know when to stop. Learn how to make AI more reliable in the places where getting it wrong really matters.

Most vector search books start with the database. This one starts with the machine. Build a search engine from scratch, then push it from brute force to billion-scale retrieval with SIMD, HNSW, quantization and distributed systems. By the end, vector search won't be a black box. It'll be something you know how to build, tune and scale.

Stop asking one giant prompt to carry your whole software system. Context Driven Development teaches you how to give AI coding agents explicit requirements, architecture decisions, constraints, tasks, and verification loops so they can work inside a reliable engineering environment.

Go beyond prompts and learn how to build AI systems that hold up in production. This book shows how to make context the foundation of reliable LLM applications, covering practical patterns, real trade-offs and proven engineering techniques with clear examples you can put to work right away.

This book shows how to structure project information so that AI produces software that is consistent, predictable, and aligned with your engineering practices.

AI has changed the way code is written, but its true impact lies in software engineering. This book explains why specification, architecture, and context have moved to the center of modern development.

A IA mudou a forma de escrever código, mas o verdadeiro impacto está na engenharia de software. Este livro explica por que especificação, arquitetura e contexto passaram a ocupar o centro do desenvolvimento moderno.

Your AI agent can sound right and still act wrong. Learn how to secure the path from a model’s suggestion to a real-world action—with practical guidance on data, permissions, tools, adversarial testing, and recovery.

Get more from Claude Sonnet 5.5 with practical prompting techniques that actually work. This guide explores instruction design, effort control, tool use, structured outputs and agentic workflows, with clear distinctions between documented behavior and practical experience. Built for developers, prompt engineers and advanced users.

What if you could build an AI that writes just like you? In Building Your LLM Twin, you'll follow a hands-on journey from raw writing data to a fully deployed AI that captures your unique voice. Learn to build, fine-tune and scale real-world LLM systems with practical code, proven techniques and the tools to take your project from concept to production.