Building and deploying vector search systems necessitates careful attention to performance and relevance trade-offs. This chapter introduces systematic approaches for fine-tuning system parameters and conducting rigorous evaluations to ensure your implementation meets its objectives.

You will learn to utilize key evaluation metrics, including Recall@k, Precision@k, and query latency, to measure system effectiveness. We will cover methods for constructing suitable ground truth datasets essential for reliable evaluation. The chapter details techniques for analyzing the sensitivity of crucial index parameters, such as efSearch in HNSW or nprobe in IVF indexes, to understand their impact on performance.

Furthermore, we will examine practical strategies for setting up A/B tests to compare different search configurations, diagnosing common relevance problems, differentiating between offline and online evaluation methodologies, and adapting tuning approaches based on specific application requirements, such as Retrieval-Augmented Generation (RAG) compared to broader semantic search tasks. The goal is to equip you with the tools to optimize and validate your advanced vector search solutions.