AIPowered Search

Build search engines powered by the latest machine learning techniques and large language models.AIPowered Search shows you how to build cuttingedge search engines that continuously learn from both your users and your content and drive more domainaware and intelligent search.Inside youll learn modern, datasciencedriven search techniques like:Semantic search using dense vector embeddings from foundation models Retrieval augmented generation Question answering and summarization combining search and LLMs Finetuning transformerbased LLMs Personalized search based on user signals and vector embeddings Collecting user behavioral signals and building signals boosting models Semantic knowledge graphs for domainspecific learning Implementing machinelearned ranking models (learning to rank) Building click models to automate machinelearned ranking Generative search, hybrid search, and the search frontierTodays search engines are expected to be smart, understanding the nuances of natural language queries, as well as each users preferences and context. This book empowers you to build search engines that take advantage of user interactions and the hidden semantic relationships in your content to automatically deliver better, more relevant search experiences. Youll even learn how to integrate large language models (LLMs) like GPT and other foundation models to massively accelerate the capabilities of your search technology.About the bookAIPowered Search is a handson guide to applying leadingedge data science techniques to search. It teaches you how to build search engines that automatically understand the intent of your users queries in order to deliver significantly more relevant search results.Youll use LLMs for embeddings, question answering, and results summarization, as well as learning how to fine tune them for the best results. Working through code in interactive notebooks, youll deploy intelligent AIpowered search systems that deliver realtime personalization and contextual understanding of each user, domain, and query through a selflearning search platform that continuously learns from evolving content and user interactions.About the readerFor software developers and data scientists familiar with the basics of search engine technology.About the authorTrey Grainger is the Founder of Searchkernel (AIpowered search), CTO of Presearch (decentralized web search), and former Chief Algorithms Officer and SVP of Engineering at Lucidworks (ecommerce, site, and enterprise search). Trey also coauthored Solr in Action (Manning 2014).Doug Turnbull is a Principal Engineer at Reddit (social discussion search), former Staff Relevance Engineer at Spotify (Ecommerce Search) and is the former Chief Technical Officer at OpenSource Connections. Doug also coauthored Relevant Search (Manning 2016).Max Irwin is the Founder of Max.io (AI model scaling) and former Managing Consultant at OpenSource Connections (search relevance consulting).