Semantic Search: Evaluation Methods and Indexing Strategies

Traditional keyword search often fails to capture the true intent behind user queries, making semantic search essential for modern applications. However, building a system that actually delivers accurate results requires rigorous evaluation and smart indexing strategies. In this course, you will transition from basic keyword matching to designing, evaluating, and maintaining robust vector-based search systems. You will learn how to measure search quality, manage embedding models safely, and update your search indexes without disrupting live users. What you'll learn: - Understand foundational semantic search concepts, key terminology, and how vector embeddings represent meaning. - Learn critical evaluation metrics like Precision, Recall, and Normalized Discounted Cumulative Gain (NDCG) to measure search quality. - Explore vector indexing strategies and database management to keep search queries fast and efficient. - Apply safe index update patterns and versioning strategies to manage embedding model upgrades without downtime. - Practice designing evaluation datasets to continuously monitor and improve search relevance over time. This text-based course guides you step-by-step from core definitions to practical system design. You will explore real-world scenarios, review written code-based examples of evaluation pipelines, and learn how to maintain high-performing search systems. This course is designed for software developers, data practitioners, and aspiring search engineers who want to build reliable search systems. No prior experience with vector databases is required, though a basic understanding of programming concepts is helpful. Start reading today to build search systems that truly understand user intent.