Improving search relevance through data-driven query optimization involves using Elasticsearch Query DSL to enhance full-text search experiences, such as FAQ or Wiki searches, by fine-tuning query parameters. The blog post outlines a structured approach to query optimization by leveraging the MS MARCO dataset, which includes 3.2 million documents and over 350,000 Bing web search queries to demonstrate the tuning of parameters for better relevance ranking. It highlights the use of tools like the Rank Evaluation API to measure search relevance with metrics such as mean reciprocal rank (MRR). The optimization process involves defining a parameter space and employing techniques like grid search and Bayesian optimization to systematically explore parameter values. The article underscores the importance of quality data and suggests that while automated tuning can significantly enhance search relevance, it does not replace manual relevance tuning. The author shares insights on optimizing queries within the MS MARCO document ranking challenge, achieving notable improvements in MRR scores through iterative testing and parameter adjustments, and encourages experiments using Elastic Cloud clusters.