Context Engineering for AI Systems: A Practical Guide to Prompt Architecture, Memory Design, RAG Pipelines, and Multi-Agent Workflows Using Modern LLM

By: Nexon, Clifford

Large Language Models don't fail because they're weak.
They fail because their context is poorly designed.
If you want reliable AI agents, accurate RAG systems, and production-ready LLM applications, you must go beyond basic prompt engineering and learn how to engineer context as a system.
Context Engineering for AI Systems is a hands-on, developer-focused guide to designing smarter, more controllable, and more scalable LLM-powered applications. This book teaches you how information flows into, through, and between large language models and how to shape that flow intentionally to unlock advanced reasoning, tool usage, and memory-aware behavior.
Rather than relying on trial and error prompts, you'll learn a full-stack approach to LLM system design, covering prompt architecture, dynamic memory, retrieval pipelines, tool integration, and multi-agent coordination.
This book is ideal for developers, AI engineers, founders, and technical product builders working with ChatGPT, Claude, open-source LLMs, LangChain-style frameworks, RAG systems, and autonomous agents.

• Format: Paperback
• Language: English
• ISBN: B0G8KWTFQB
• ISBN13: 9798278635390
• Release Date: December 2025
• Publisher: Independently Published
• Length: 106 Pages
• Weight: 0.44 lbs.
• Dimensions: 0.2" x 7.0" x 10.0"