Paperback. Context Engineering for Modern AI: Strategies for Precision Prompting, Memory Management, and Reliable Agent WorkflowsModern AI systems don't fail because they lack intelligence, they fail because they lack context. As models grow more capable, the real skill is no longer simply "prompting." The real skill is engineering clear, stable, and reliable context that shapes how models think, reason, act, and collaborate.This book gives you the complete foundation you need to design, build, and maintain high-reliability AI systems through structured prompts, memory architectures, retrieval pipelines, tool-calling strategies, and multi-agent workflows. Whether you're a beginner trying to understand why some prompts work and others fail, or an experienced developer building production-grade AI systems, this book guides you with clarity and technical depth, without unnecessary jargon.You will learn how to construct effective prompts, create and manage long-term memory, build retrieval-augmented reasoning systems, engineer tool-based interactions, orchestrate multi-agent teams, and test AI behavior with the same rigor used in software engineering. The book introduces a practical framework that breaks context into layers-instruction, memory, data, and constraints, and shows how to use these layers to shape predictable model behavior.Through detailed explanations and real-world examples, you'll understand why models hallucinate, why workflows drift during long tasks, why agents lose track of goals, and how to prevent these problems with structured design. You will also learn how to build context servers, create self-healing workflows, scale hybrid pipelines, and enforce enterprise-level guardrails for safety and auditing.This book is more than a guide; it is a blueprint for engineering AI that thinks clearly, reasons reliably, and completes work with confidence. Every chapter focuses on practical methods, actionable patterns, and proven techniques drawn from modern AI engineering practices.What You Will Master- Precision prompting that shapes tone, behavior, and structure.- Context frameworks that reduce hallucination and improve reasoning.- Memory systems that prevent drift and enable long-running tasks.- Retrieval-augmented generation pipelines for high factual accuracy.- Tool-calling design for predictable interactions with external systems.- Multi-agent architectures that support coordination and specialization.- Testing, debugging, and evaluation methods for AI behavior.- Production patterns and safety principles for enterprise deployments.-Advanced techniques such as adaptive prompting, cross-model orchestration, and self-healing context loops.If you want to build AI systems that don't guess, systems that reason, retrieve, plan, coordinate, and complete real work, this book gives you the foundation and the practical toolkit to make it happen.Step into the next stage of AI development.Learn how to engineer context with precision.Start building AI you can trust.Your future AI systems begin with the context you design today. This item is printed on demand. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability.

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Bibliographic Details

Title: Context Engineering for Modern AI (Paperback)
Publisher: Independently Published
Publication Date: 2025
Language: English
ISBN 13: 9798277869109
Binding: Paperback
Condition: new

About this title

Synopsis

Context Engineering for Modern AI: Strategies for Precision Prompting, Memory Management, and Reliable Agent Workflows
Modern AI systems don’t fail because they lack intelligence, they fail because they lack context. As models grow more capable, the real skill is no longer simply “prompting.” The real skill is engineering clear, stable, and reliable context that shapes how models think, reason, act, and collaborate.
This book gives you the complete foundation you need to design, build, and maintain high-reliability AI systems through structured prompts, memory architectures, retrieval pipelines, tool-calling strategies, and multi-agent workflows. Whether you're a beginner trying to understand why some prompts work and others fail, or an experienced developer building production-grade AI systems, this book guides you with clarity and technical depth, without unnecessary jargon.
You will learn how to construct effective prompts, create and manage long-term memory, build retrieval-augmented reasoning systems, engineer tool-based interactions, orchestrate multi-agent teams, and test AI behavior with the same rigor used in software engineering. The book introduces a practical framework that breaks context into layers—instruction, memory, data, and constraints, and shows how to use these layers to shape predictable model behavior.
Through detailed explanations and real-world examples, you'll understand why models hallucinate, why workflows drift during long tasks, why agents lose track of goals, and how to prevent these problems with structured design. You will also learn how to build context servers, create self-healing workflows, scale hybrid pipelines, and enforce enterprise-level guardrails for safety and auditing.
This book is more than a guide; it is a blueprint for engineering AI that thinks clearly, reasons reliably, and completes work with confidence. Every chapter focuses on practical methods, actionable patterns, and proven techniques drawn from modern AI engineering practices.
What You Will Master
• Precision prompting that shapes tone, behavior, and structure.
• Context frameworks that reduce hallucination and improve reasoning.
• Memory systems that prevent drift and enable long-running tasks.
• Retrieval-augmented generation pipelines for high factual accuracy.
• Tool-calling design for predictable interactions with external systems.
• Multi-agent architectures that support coordination and specialization.
• Testing, debugging, and evaluation methods for AI behavior.
• Production patterns and safety principles for enterprise deployments.
•Advanced techniques such as adaptive prompting, cross-model orchestration, and self-healing context loops.
If you want to build AI systems that don’t guess, systems that reason, retrieve, plan, coordinate, and complete real work, this book gives you the foundation and the practical toolkit to make it happen.
Step into the next stage of AI development.
Learn how to engineer context with precision.
Start building AI you can trust.
Your future AI systems begin with the context you design today.

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