SEARCH SYSTEMS : Ranking and Relevance : Building Intelligent Retrieval Engines for the Modern AI Era by Mohan Rayithi
English | March 12, 2026 | ISBN: N/A | ASIN: B0GS8VSSYZ | 515 pages | EPUB | 0.84 Mb
Search powers the modern digital world. But how does it really work?
From Google-scale ranking systems to AI-driven semantic retrieval, Search Systems: Ranking & Relevance reveals the engineering principles behind the world's most powerful search engines.
This book takes you deep inside the architecture of modern search platforms - explaining how documents are indexed, how queries are understood, and how intelligent algorithms rank the most relevant results from billions of documents in milliseconds.
Designed for engineers, AI practitioners, data scientists, and system architects, this practical guide covers both the classical foundations of information retrieval and the cutting-edge technologies shaping the future of search, including neural retrieval, vector databases, multimodal search, AI agents, and retrieval-augmented generation.
Inside this book you will learn:
* How modern search engines are architected at scale
* The mathematics behind ranking algorithms such as TF-IDF and BM25
* How Learning-to-Rank systems optimize relevance
* How semantic search and embeddings transform information retrieval
* How vector databases power large-scale similarity search
* How AI agents and autonomous retrieval systems are redefining search
* How Retrieval Augmented Generation connects search with generative AI
* How to design enterprise search, conversational search, and multimodal systems
Packed with real-world case studies, hands-on projects, and practical engineering insights, this book is a complete guide to building and understanding modern search technology.
Whether you are building AI search systems, enterprise knowledge platforms, recommendation engines, or next-generation AI assistants, this book will give you the knowledge and tools to design search systems that truly scale.
Search is no longer just about finding documents.
It is about discovering knowledge.
Links are Interchangeable - Single Extraction
