Designing Retrieval, Ranking, and RAG Systems That Actually Work Large Language Models changed how we interact with information—but they didn’t solve search. In fact, they broke it. Teams everywhere rushed to build “chat with your data” systems, only to discover hallucinations, irrelevant answers, slow responses, and fragile architectures. The problem wasn’t the LLM. It was the retrieval. Modern Search Infrastructure for the LLM Era is a practical, engineering-first guide to the most important—and most misunderstood—layer of modern AI systems: search and retrieval. This book shows why success in the LLM era is no longer about picking the biggest model, but about building a high-quality retrieval pipeline that feeds models the right context at the right time. It bridges decades of classical Information Retrieval with cutting-edge techniques like vector search, hybrid retrieval, reranking, Retrieval-Augmented Generation (RAG), and agentic AI. You’ll learn how modern search systems actually work—and how to design them to scale, adapt, and stay grounded in reality. What you’ll learn Why naive RAG systems fail—and how to fix them - How classical IR (BM25, inverted indexes, ranking cascades) still underpins modern AI - How vector embeddings really work, and how to deploy them at scale - How to design hybrid retrieval systems that balance precision and recall - How reranking, evaluation, and feedback loops determine answer quality - How search becomes memory for agents and AI systems - How to build production-grade, governable, and cost-efficient retrieval infrastructure Who this book is for Software and ML engineers building AI-powered applications - Search, platform, and infrastructure engineers - Architects designing RAG and agentic systems - Technical product leaders responsible for AI quality and reliability This is not a shallow tutorial or a collection of blog-level recipes. It’s a blueprint —grounded in real systems, real failure modes, and real production constraints. The next breakthrough in AI won’t come from a bigger model. It will come from a better way to retrieve knowledge. If you’re serious about building AI systems that work in the real world, this book is your foundation.