RAG and Context Engineering

In a world where AI systems are scaling faster than ever, the ability to engineer context effectively has become the deciding factor between mediocre performance and breakthrough results. RAG and Context Engineering: Master Context Windows, Long-Term Memory, and Retrieval-Augmented Generation for Scalable AI is your definitive guide to building AI applications that think smarter, remember longer, and deliver more relevant answers - every time.

This book takes you inside the cutting-edge techniques behind context windows, long-term memory integration, and retrieval-augmented generation (RAG). You'll discover how to structure, filter, and scale AI knowledge bases, seamlessly combine memory with vector search, and deploy production-ready systems capable of handling thousands - even millions - of documents.

Written by Roberto Pizzlo, a seasoned AI systems engineer and technical author, this guide blends practical workflows with real-world examples to ensure you can execute every concept, not just understand it. Whether you are an AI developer, machine learning engineer, data scientist, or tech entrepreneur, you'll gain the expertise to:

• Architect scalable RAG pipelines with precision

• Integrate semantic search, ranking, and multi-source retrieval

• Optimize context usage for cost, speed, and accuracy

• Maintain and update AI knowledge bases with confidence

• Future-proof your workflows for larger context windows and emerging AI architectures

With its clear, actionable, and execution-ready approach, this book delivers the depth you need without unnecessary fluff. You'll finish with the ability to not only design smarter AI but also ensure it adapts to the fast-evolving landscape of autonomous, context-driven systems.

If you want to master context engineering, vector search, and RAG at scale, this is the playbook trusted by forward-thinking AI professionals who need results now - and are ready to lead the next wave of intelligent applications.