Apply cutting-edge Machine Learning techniques–from crowdsourced relevance and knowledge graph learning, to Large Language Models (LLMs)–to enhance the accuracy and relevance of your search results. Delivering effective search is one of the biggest challenges you can face as an engineer. AI-Powered Search is an in-depth guide to building intelligent search systems you can be proud of. It covers the critical tools you need to automate ongoing relevance improvements within your search applications. This book is for search engineers, software engineers, and data scientists who want to learn how to build cutting-edge search engines integrating the latest Machine Learning techniques to drive more domain-aware and intelligent search. The book also provides a thorough overview of AI-powered search for product managers and business leaders who may not be able to implement the techniques themselves, but who want to understand the possibilities and limitations of AI-powered search. Technical readers who want to get the most out of this book can follow along with the Python code examples. Familiarity with SQL (Structured Query Language) syntax is assumed, as we’ve chosen to implement many of the data aggregations in this standardized representation when possible. A basic understanding of how search engines (such as Elasticsearch, Apache Solr, or OpenSearch) or vector databases work is also helpful, but not required.

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