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{ "item_title" : "Building AI Systems with Context Engineering", "item_author" : [" Alira Vexel "], "item_description" : "Building AI Systems with Context Engineering: Architecting Reliable LLM Systems with RAG, Memory Layers, and Prompt ProtocolsAre your AI systems struggling with hallucinations, lost memory, or inconsistent tool use?Discover the cutting-edge discipline of context engineering - the missing layer in today's LLM workflows - and learn how to build reliable, context-aware AI systems from the ground up using retrieval-augmented generation (RAG), dynamic memory, and structured prompt protocols.This practical blueprint goes beyond theory to help developers, architects, and engineers design, build, and deploy production-grade LLM pipelines that retain memory, optimize context windows, and integrate tools dynamically.What You'll Learn Inside: Build modular context layers: prompt → memory → retrieval → tool injectionImplement RAG systems with ChromaDB, Weaviate, and LangChainEngineer long- and short-term memory using vector stores and semantic summarizationCreate role-specific prompts, dynamic agent flows, and fallback routinesEvaluate LLM pipelines using AutoEval, Promptfoo, and LangSmithDeploy CI/CD pipelines for versioned prompts and context-aware agentsTroubleshoot prompt injection, token overflow, and irrelevant chunk retrievalMaster LangGraph, CrewAI, and AutoGen for multi-agent orchestrationIncludes: Fully worked code representations in PythonReal-world tools: GPT-4o, Claude 3, Qwen, Mixtral, Zep, OpenRouter, PromptLayerDeployment-ready recipes, workflow templates, and memory architecture diagramsAppendices with reusable prompt templates, YAML context blocks, and vector store setupsWhether you're building an intelligent chatbot, a scalable RAG app, or a multi-agent pipeline, this book gives you everything you need to engineer context as a first-class citizen in modern AI systems.Perfect for: LLM Developers, AI Engineers, Technical Architects, and Builders of Next-Gen AIStart building smarter AI today.Master context. Unlock reliability. Engineer intelligence.", "item_img_path" : "https://covers1.booksamillion.com/covers/bam/9/79/829/606/9798296064776_b.jpg", "price_data" : { "retail_price" : "19.99", "online_price" : "19.99", "our_price" : "19.99", "club_price" : "19.99", "savings_pct" : "0", "savings_amt" : "0.00", "club_savings_pct" : "0", "club_savings_amt" : "0.00", "discount_pct" : "10", "store_price" : "" } }
Building AI Systems with Context Engineering|Alira Vexel

Building AI Systems with Context Engineering : Architecting Reliable LLM Systems with RAG, Memory Layers, and Prompt Protocols

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Overview

Building AI Systems with Context Engineering: Architecting Reliable LLM Systems with RAG, Memory Layers, and Prompt Protocols

Are your AI systems struggling with hallucinations, lost memory, or inconsistent tool use?

Discover the cutting-edge discipline of context engineering - the missing layer in today's LLM workflows - and learn how to build reliable, context-aware AI systems from the ground up using retrieval-augmented generation (RAG), dynamic memory, and structured prompt protocols.

This practical blueprint goes beyond theory to help developers, architects, and engineers design, build, and deploy production-grade LLM pipelines that retain memory, optimize context windows, and integrate tools dynamically.

What You'll Learn Inside:

Build modular context layers: prompt → memory → retrieval → tool injection
Implement RAG systems with ChromaDB, Weaviate, and LangChain
Engineer long- and short-term memory using vector stores and semantic summarization

  • Create role-specific prompts, dynamic agent flows, and fallback routines
  • Evaluate LLM pipelines using AutoEval, Promptfoo, and LangSmith
  • Deploy CI/CD pipelines for versioned prompts and context-aware agents
  • Troubleshoot prompt injection, token overflow, and irrelevant chunk retrieval
  • Master LangGraph, CrewAI, and AutoGen for multi-agent orchestration

Includes:

  • Fully worked code representations in Python
  • Real-world tools: GPT-4o, Claude 3, Qwen, Mixtral, Zep, OpenRouter, PromptLayer
  • Deployment-ready recipes, workflow templates, and memory architecture diagrams
  • Appendices with reusable prompt templates, YAML context blocks, and vector store setups

Whether you're building an intelligent chatbot, a scalable RAG app, or a multi-agent pipeline, this book gives you everything you need to engineer context as a first-class citizen in modern AI systems.

Perfect for: LLM Developers, AI Engineers, Technical Architects, and Builders of Next-Gen AI

Start building smarter AI today.
Master context. Unlock reliability. Engineer intelligence.

This item is Non-Returnable

Details

  • ISBN-13: 9798296064776
  • ISBN-10: 9798296064776
  • Publisher: Independently Published
  • Publish Date: August 2025
  • Dimensions: 11 x 8.5 x 0.53 inches
  • Shipping Weight: 1.3 pounds
  • Page Count: 252

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