Practical Context Engineering for AI systems: Design Smarter AI with Langchain, RAG, LlamaIndex and Multi-Agent Context Protocol for Context-Aware Reasoning
Practical Context Engineering for AI systems: Design Smarter AI with Langchain, RAG, LlamaIndex and Multi-Agent Context Protocol for Context-Aware Reasoning
In a world where Large Language Models (LLMs) are reshaping every industry, the real secret to building smarter, faster, and more adaptive AI systems lies not in the model itself - but in how you design and manage context. Whether you're a developer, data scientist, AI engineer, or startup founder, Practical Context Engineering for AI Systems is your ultimate field manual for mastering the next frontier of AI architecture. Inside this book, you'll learn how to: - Understand what context really means in LLM ...
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In a world where Large Language Models (LLMs) are reshaping every industry, the real secret to building smarter, faster, and more adaptive AI systems lies not in the model itself - but in how you design and manage context. Whether you're a developer, data scientist, AI engineer, or startup founder, Practical Context Engineering for AI Systems is your ultimate field manual for mastering the next frontier of AI architecture. Inside this book, you'll learn how to: - Understand what context really means in LLM-powered systems - from semantic memory to token budgeting - Build intelligent agents using LangChain, LlamaIndex, DSPy, and Zep - Master Retrieval-Augmented Generation (RAG) pipelines and hybrid vector search - Implement long-term memory, dynamic prompts, and real-time context switching -Design agentic workflows with the Multi-Agent Context Protocol (MCP) - Optimize for scale with modular architectures, vector DBs, and memory orchestration - Deploy real-world AI projects like research assistants, tutors, support agents, and more This hands-on, project-driven guide walks you through 11 deeply practical chapters and realistic agent deployments -equipping you with the tools, code patterns, and architectural strategies needed to make LLMs context-aware, responsive, and production-ready. Whether you're injecting knowledge into prompts, integrating memory, or designing multi-agent ecosystems, this book will show you how to go from basic prompt engineering to full-stack context mastery - all from scratch. Key Technologies Covered: - LangChain, LlamaIndex, DSPy, Zep - Vector Search: Chroma, FAISS, Pinecone, Weaviate -Embeddings: OpenAI, Hugging Face, BGE - Retrieval-Augmented Generation (RAG) - Multi-Agent Context Protocol (MCP) - LangGraph, AutoGen, Redis, and more What Makes This Book Stand Out? - SEO-rich, high-demand topics: LangChain, RAG, Zep, vector DBs, DSPy, and agentic workflows - Comprehensive frameworks: Covers foundational theory, tooling, and deployment - From beginner to expert: No fluff, no filler - just actionable content - Real-world use cases: Customer support bots, tutors, writers, researchers, and memory-based assistants - Bonus Resources: Cheatsheets, setup guides, modular templates, and GitHub access If you're serious about building context-aware AI agents , mastering semantic memory , and unlocking the full potential of LLMs - this is the only book you'll need.
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Add this copy of Practical Context Engineering for AI systems: Design to cart. £19.85, new condition, Sold by Ingram Customer Returns Center rated 5.0 out of 5 stars, ships from NV, USA, published 2025 by Independently Published.
