SKU/Artículo: AMZ-B0GQCX26DL
Designing Reliable LLM Systems: A Practical Framework for Prompt Architecture, Retrieval, Memory, and Agent Workflows LangChain, MCP
Format:
Paperback
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En stock
En stock
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0.31 kg
0.31 kg
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Sí
Sí
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Nuevo
Nuevo
Producto de:
Amazon
Amazon
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USA
USA
Sobre este producto
- Large Language Models (LLMs) are not just powerful because of their size they are powerful because of how they use context. Yet most developers still treat context as an afterthought, relying on ad hoc prompts, unstable retrieval pipelines, and brittle memory systems that break in real-world applications. Designing Reliable LLM Systems changes that. This book introduces a rigorous, system-level approach to building dependable, scalable, and reasoning-capable AI applications. Instead of focusing narrowly on prompt engineering tricks, it reframes the problem around context architecture how information is selected, structured, stored, retrieved, transformed, and coordinated across multiple AI components. You will learn how to move beyond simple question-and-answer interactions to construct production-grade AI systems that can plan, retrieve knowledge, collaborate across agents, maintain continuity, and adapt dynamically to changing information. Through carefully designed frameworks, patterns, and case studies, the book shows how context flows from databases and tools into prompts, through memory layers, and back into actionable responses. The text blends theory with hands-on practice. You will work with modern AI orchestration tools such as LangChain, MCP, LangGraph, and RAG pipelines, exploring how they can be composed into robust multi-agent workflows. You will also learn systematic methods for compressing, pruning, and ranking context so that systems remain reliable even under strict token limits. A major focus of the book is trustworthy AI behavior. You will study why hallucinations occur, how memory leakage and state drift emerge in long-running agents, and how to design safeguards that keep systems consistent, explainable, and auditable. Real-world debugging techniques using tools like LangSmith and PromptLayer are woven throughout the chapters. By the end of this book, you will be equipped to design intelligent support bots, planning assistants, research copilots, and enterprise SaaS agents that do more than respond; they reason, collaborate, and scale. This is a practical guide for developers, AI engineers, technical product leads, and researchers who want to move from experimentation to dependable deployment. Master context. Master modern AI.
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