How to improve Elasticsearch search relevance with boolean queries

The blog post by Alex Marquardt discusses strategies for enhancing search relevance in Elasticsearch through the use of boolean queries, match queries, and match phrase queries. It explains how Elasticsearch ranks search results using a scoring algorithm that considers factors like term frequency, inverse document frequency, and field length. The post provides examples of how different query operators, such as OR and AND, can affect the relevance and ranking of search results, demonstrating the flexibility of Elasticsearch in tuning search outputs to match specific use cases. By combining these queries with a boolean query's should clause, users can balance between exact matches and broader results, with the option to boost specific clauses to prioritize certain results. The article also introduces search templates for managing complex queries and suggests additional resources for further relevance tuning.

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