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.
