Designing Search Ranking Systems: Document Selection and Retrieval

Behind every fast search engine lies a sophisticated system designed to filter billions of documents down to the most relevant few in milliseconds. Understanding how these document selection and retrieval pipelines work is essential for building modern search applications. In this written course, you will develop a solid foundation in search architecture, moving from basic keyword matching to advanced multi-stage ranking pipelines. You will gain the conceptual clarity and practical knowledge needed to evaluate, design, and optimize document selection systems for various search use cases. What you'll learn: - Understand the fundamental terminology of information retrieval, including inverted indexes, tokenization, and text normalization. - Analyze classic relevance scoring algorithms such as TF-IDF and BM25 to understand how keyword-based retrieval works. - Explore modern dense retrieval techniques using vector embeddings and vector databases for semantic search. - Implement hybrid search strategies that combine the precision of sparse keyword matching with the conceptual depth of dense retrieval. - Evaluate multi-stage ranking pipelines, including candidate generation and re-ranking phases. - Apply evaluation metrics like Precision, Recall, MAP, and NDCG to measure and improve search quality. The course begins with foundational concepts of indexing and text processing before guiding you through classic scoring algorithms, semantic vector search, and multi-stage architectures. You will read clear explanations, walk through structured pseudocode, and study practical design patterns. This course is designed for software engineers, data analysts, and product managers new to search technology. No prior experience with information retrieval is required, as we start with core definitions and build up to modern architectures. Start reading today to build faster, more relevant search experiences.