June 2027
Intermediate
300 pages
1h 52m
English
Overview
To deliver reliable and maintainable AI systems, we need to approach the task with an engineering mindset. The Context Engineering Handbook introduces the emerging discipline of context engineering, offering practical tools for building LLM applications that are robust, focused, and reliable. Drawing from experiences and use cases from industry experts, Drew Breunig brings together experienced practitioners with technical insight and real-world examples to show how poor context design undermines AI performance and how thoughtful structuring can unlock real value in production systems.
This book goes beyond prompt hacks and explores a repeatable, systematic approach to building context for agents, applications, and pipelines. Through clear frameworks and detailed case studies, you'll learn how to turn messy ad hoc implementations into scalable, maintainable systems. Whether you're debugging unpredictable agent behavior or designing new AI-powered tools, this book will help you structure your work more effectively from the start.
- Identify common failure modes in AI context management
- Design clear, modular contexts that improve system accuracy
- Structure pipelines to support focus, relevance, and reuse
- Translate raw data and business inputs into useful AI context
- Explore real-world examples of context design in practice
- Build more maintainable, trustworthy LLM-powered systems
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