SKU/Artículo: AMZ-B0FK1GGKK4
Applied Context Engineering for LLMs: A Practical Guide to Building Modular, Interpretable NLP Systems with LangGraph, RAG Pipelines, and Prompt-Based Design
Format:
Kindle
Detalles del producto
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En stock
En stock
Peso con empaque:
0.15 kg
0.15 kg
Devolución:
Sí
Sí
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Nuevo
Nuevo
Producto de:
Amazon
Amazon
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USA
USA
Sobre este producto
- Unlock the true potential of language models. In Applied Context Engineering for LLMs, Zak Akhtar introduces a groundbreaking approach to building modular, interpretable, and contextually aware NLP systems. If you’ve struggled with managing large language models (LLMs) in real-world applications, this book is your ultimate solution. Packed with practical insights, this guide walks you through the complex world of context engineering, teaching you how to design systems that remember, reason, and adapt based on contextual inputs and external data retrieval. From LangGraph workflows to RAG pipelines, the techniques presented in this book empower you to craft intelligent, dynamic, and scalable NLP systems that work reliably across multiple sessions. Key Benefits: Build production-ready LLM systems that manage memory, retrieval, and task flow. Understand how to deal with contextual failures, hallucinations, and model drift in production systems. Learn how to design systems that are both modular and interpretable, making it easier to debug and maintain. Gain hands-on experience with the tools and frameworks that are driving the future of AI-powered NLP systems. This book stands out by bridging the gap between theory and real-world application. It isn’t just about writing better prompts—it’s about designing systems that engineer context from the ground up, ensuring robust performance, scalability, and long-term adaptability. Whether you're a developer, data scientist, or NLP enthusiast, you’ll find actionable advice and code examples that directly address the challenges of deploying and maintaining LLMs in diverse environments. Get your copy today and start building AI systems that learn, remember, and scale with precision.
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