Context Engineering is a fundamental skill that enables the creation of more accurate, manageable, and reliable AI systems. In this book, you will learn how to properly structure, select, and update data so that large language models operate stably even in conditions of expanding context windows and rapidly changing information.
What You Will Learn
Key Concepts and Methods
This book combines techniques of prompt engineering, intelligent search, data filtering, and modern RAG approaches. Through practical examples, it explains how to:
- select optimal information sources;
- build effective pipelines;
- prevent errors caused by noisy and inconsistent data;
- think architecturally with context just as with programming code.
Practical Tools and Frameworks
Working with Modern Ecosystems
An analysis of current libraries and frameworks allows you to implement context engineering techniques in real projects. The book covers:
- DSPy — automation and optimization of prompts;
- LangChain — building complex LLM pipelines;
- CrewAI — development and management of agent systems;
- LlamaIndex — working with corporate knowledge bases.
You will also learn how to effectively use flagship models from OpenAI, Anthropic, and Google in applied tasks.
Book Contents
Section I. Basics of Context Engineering
Basic Architectural Approaches
- RAG Architectures: creating pipelines that ground AI responses on external data;
- Memory Management: integrating short-term and long-term memory for stable model performance.
Section II. Working with Agents
Designing and Optimizing Behavior
- Workflow Design: managing states and logic of multi-step processes;
- Complex Agent Systems: creating autonomous AI agents for tasks of high complexity.
Section III. Quality and Observability
Controlling Stability and Accuracy
- Context Evaluation: identifying errors, hallucinations, and hidden failures;
- Observability: analyzing AI performance through logs, tracing, tokens, and interaction with external tools.
Who This Book Is For
This book is intended for AI engineers, data scientists, and technical leaders who are already familiar with the basics of LLM and want to create scalable, predictable, and manageable AI applications. It will serve as a reliable foundation for those looking to enhance the accuracy, transparency, and resilience of their models.
