{"product_id":"ai-powered-search-isbn-9781617296970","title":"AI-Powered Search","description":"\u003cb\u003eApply 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.\u003c\/b\u003e\u003cbr\u003e\u003cbr\u003eDelivering effective search is one of the biggest challenges you can face as an engineer. \u003ci\u003eAI-Powered Search\u003c\/i\u003e 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.\u003cbr\u003e\u003cbr\u003eInside you’ll learn modern, data-science-driven search techniques like:\u003cul\u003e\n\u003cli\u003eSemantic search using dense vector embeddings from foundation models\u003c\/li\u003e\n\u003cli\u003eRetrieval augmented generation (RAG)\u003c\/li\u003e\n\u003cli\u003eQuestion answering and summarization combining search and LLMs\u003c\/li\u003e\n\u003cli\u003eFine-tuning transformer-based LLMs\u003c\/li\u003e\n\u003cli\u003ePersonalized search based on user signals and vector embeddings\u003c\/li\u003e\n\u003cli\u003eCollecting user behavioral signals and building signals boosting models\u003c\/li\u003e\n\u003cli\u003eSemantic knowledge graphs for domain-specific learning\u003c\/li\u003e\n\u003cli\u003eSemantic query parsing, query-sense disambiguation, and query intent classification\u003c\/li\u003e\n\u003cli\u003eImplementing machine-learned ranking models (Learning to Rank)\u003c\/li\u003e\n\u003cli\u003eBuilding click models to automate machine-learned ranking\u003c\/li\u003e\n\u003cli\u003eGenerative search, hybrid search, multimodal search, and the search frontier\u003c\/li\u003e\n\u003c\/ul\u003e\u003ci\u003eAI-Powered Search\u003c\/i\u003e will help you build the kind of highly intelligent search applications demanded by modern users. Whether you’re enhancing your existing search engine or building from scratch, you’ll learn how to deliver an AI-powered service that can continuously learn from every content update, user interaction, and the hidden semantic relationships in your content. You’ll learn both how to enhance your AI systems with search and how to integrate large language models (LLMs) and other foundation models to massively accelerate the capabilities of your search technology.\u003cbr\u003e\u003cbr\u003eForeword by \u003cb\u003eGrant Ingersoll\u003c\/b\u003e.\u003cbr\u003e\u003cbr\u003e\u003cb\u003eAbout the technology\u003c\/b\u003e\u003cbr\u003e\u003cbr\u003eModern search is more than keyword matching. Much, much more. Search that learns from user interactions, interprets intent, and takes advantage of AI tools like large language models (LLMs) can deliver highly targeted and relevant results. This book shows you how to up your search game using state-of-the-art AI algorithms, techniques, and tools.\u003cbr\u003e\u003cbr\u003e\u003cb\u003eAbout the book\u003c\/b\u003e\u003cbr\u003e\u003cbr\u003e\u003ci\u003eAI-Powered Search\u003c\/i\u003e teaches you to create a search that understands natural language and improves automatically the more it is used. As you work through dozens of interesting and relevant examples, you’ll learn powerful AI-based techniques like semantic search on embeddings, question answering powered by LLMs, real-time personalization, and Retrieval Augmented Generation (RAG).\u003cbr\u003e\u003cbr\u003e\u003cb\u003eWhat's inside\u003c\/b\u003e\u003cul\u003e\n\u003cli\u003eSparse lexical and embedding-based semantic search\u003c\/li\u003e\n\u003cli\u003eQuestion answering, RAG, and summarization using LLMs\u003c\/li\u003e\n\u003cli\u003ePersonalized search and signals boosting models\u003c\/li\u003e\n\u003cli\u003eLearning to Rank, multimodal, and hybrid search\u003c\/li\u003e\n\u003c\/ul\u003e\u003cb\u003eAbout the reader\u003c\/b\u003e\u003cbr\u003e\u003cbr\u003eFor software developers and data scientists familiar with the basics of search engine technology.\u003cbr\u003e\u003cbr\u003e\u003cb\u003eAbout the author\u003c\/b\u003e\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTrey Grainger\u003c\/b\u003e is the Founder of Searchkernel and former Chief Algorithms Officer and SVP of Engineering at Lucidworks. \u003cb\u003eDoug Turnbull\u003c\/b\u003e is a Principal Engineer at Reddit and former Staff Relevance Engineer at Spotify. \u003cb\u003eMax Irwin\u003c\/b\u003e is the Founder of Max.io and former Managing Consultant at OpenSource Connections.\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTable of Contents\u003c\/b\u003e\u003cbr\u003ePart 1\u003cbr\u003e1 Introducing \u003ci\u003eAI-powered search\u003c\/i\u003e\u003cbr\u003e2 Working with natural language\u003cbr\u003e3 Ranking and content-based relevance\u003cbr\u003e4 Crowdsourced relevance\u003cbr\u003ePart 2\u003cbr\u003e5 Knowledge graph learning\u003cbr\u003e6 Using context to learn domain-specific language\u003cbr\u003e7 Interpreting query intent through semantic search\u003cbr\u003ePart 3\u003cbr\u003e8 Signals-boosting models\u003cbr\u003e9 Personalized search\u003cbr\u003e10 Learning to rank for generalizable search relevance\u003cbr\u003e11 Automating learning to rank with click models\u003cbr\u003e12 Overcoming ranking bias through active learning\u003cbr\u003ePart 4\u003cbr\u003e13 Semantic search with dense vectors\u003cbr\u003e14 Question answering with a fine-tuned large language model\u003cbr\u003e15 Foundation models and emerging search paradigms\u003cbr\u003eA Running the code examples\u003cbr\u003eB Supported search engines and vector database","brand":"Simon \u0026 Schuster","offers":[{"title":"Default Title","offer_id":48682536272101,"sku":"NP9781617296970","price":69.99,"currency_code":"USD","in_stock":false}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/1842\/7735\/files\/O_d43f8df6-edde-453e-9b58-6a8143f6e865.jpg?v=1775168223","url":"https:\/\/k12savings.com\/products\/ai-powered-search-isbn-9781617296970","provider":"K12savings","version":"1.0","type":"link"}