LLM Context Engineering Essentials: Design Better Prompts, Harness Context Memory, and Build Intelligent Agents with LangChain, RAG, and the Model Con
暫譯: LLM 上下文工程基礎：設計更佳的提示、利用上下文記憶，並使用 LangChain、RAG 和模型連接構建智能代理

LLM Context Engineering Essentials is your complete guide to designing effective prompts, managing context memory, and building intelligent, reliable applications powered by large language models. Whether you're a developer, engineer, researcher, or tech leader, this book gives you the practical tools and frameworks to take AI systems beyond simple text generation and into structured, context-aware solutions.

Inside, you'll learn how to:

• Craft prompts that drive accuracy and consistency in outputs.
• Manage context windows and token limits using proven strategies.
• Implement short-term and long-term memory in LLM-powered systems.
• Build Retrieval-Augmented Generation (RAG) pipelines for knowledge-grounded answers.
• Leverage LangChain, LlamaIndex, and the Model Context Protocol (MCP) for scalable applications.
• Design multi-agent systems that collaborate and share context seamlessly.
• Evaluate and debug your models with metrics, tools, and real-world case studies.
• Apply ethical guardrails to ensure safety, transparency, and trustworthiness.

With clear explanations, hands-on examples, and industry-driven practices, this book is written to help both beginners and advanced practitioners master the art of context engineering.

Context is the key to unlocking the true potential of LLMs. Without it, outputs are shallow, inconsistent, or unreliable. With it, you can build assistants, research tools, and intelligent agents that perform at professional standards. Don't just learn about AI, build systems that think with context. Whether you're working on chatbots, compliance tools, tutoring assistants, or enterprise AI, this book gives you the blueprint for success.

Get your copy today and start designing context-aware AI systems that are accurate, transparent, and built for the future.