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Context Engineering for LLMs: Build Smarter AI with Retrieval, Memory, and Tool Integration
The future of AI isn’t just about larger models—it’s about smarter context.
As LLMs become more powerful, the challenge isn’t raw capability but how you structure, deliver, and evolve the context they rely on. Whether you're building intelligent chatbots, retrieval-augmented generation (RAG) systems, or multi-agent frameworks, this book gives you the tools to build smarter, faster, and more responsible AI.
Context Engineering for LLMs is a practical, systems-focused guide by AI strategist Manthan M Y that demystifies how to turn static prompts into scalable context-aware architectures. You’ll learn how to combine memory, tools, and retrieval to push LLMs beyond simple tasks—into reasoning, autonomy, and real-world integration.
what you'll get
25 deeply optimized context engineered prompts25 well researched domain specific context engineering prompt templatestotal 50 templates What You'll
What context really means for LLMs—and why it's the new frontier of performance
How to use retrieval-augmented generation (RAG) with vector databases
How to design memory systems, chunking pipelines, and context agents
How to integrate tools, APIs, and real-time data into your LLM workflows
How to align AI systems with governance frameworks like the EU AI Act, ISO 42001, and NIST RMF
Who This Book Is
AI developers and machine learning engineers
NLP researchers and prompt engineers
Founders and product builders working with LLMs
Anyone deploying LLMs like GPT-4, Claude, Gemini, or open-source models in production
This book bridges the gap between theory and practice—helping you design smarter AI, not just stronger models.
As LLMs become more powerful, the challenge isn’t raw capability but how you structure, deliver, and evolve the context they rely on. Whether you're building intelligent chatbots, retrieval-augmented generation (RAG) systems, or multi-agent frameworks, this book gives you the tools to build smarter, faster, and more responsible AI.
Context Engineering for LLMs is a practical, systems-focused guide by AI strategist Manthan M Y that demystifies how to turn static prompts into scalable context-aware architectures. You’ll learn how to combine memory, tools, and retrieval to push LLMs beyond simple tasks—into reasoning, autonomy, and real-world integration.
what you'll get
25 deeply optimized context engineered prompts25 well researched domain specific context engineering prompt templatestotal 50 templates What You'll
What context really means for LLMs—and why it's the new frontier of performance
How to use retrieval-augmented generation (RAG) with vector databases
How to design memory systems, chunking pipelines, and context agents
How to integrate tools, APIs, and real-time data into your LLM workflows
How to align AI systems with governance frameworks like the EU AI Act, ISO 42001, and NIST RMF
Who This Book Is
AI developers and machine learning engineers
NLP researchers and prompt engineers
Founders and product builders working with LLMs
Anyone deploying LLMs like GPT-4, Claude, Gemini, or open-source models in production
This book bridges the gap between theory and practice—helping you design smarter AI, not just stronger models.
104 pages, Kindle Edition
Published August 2, 2025
3 people are currently reading
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