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 ...
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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.
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