AI-Powered Search
59,98 €
inkl. gesetzl. MwSt.Beschreibung
Produktdetails
Format
ePUB
Kopierschutz
Ja
Family Sharing
Ja
Text-to-Speech
Ja
Erscheinungsdatum
04.02.2025
Verlag
ManningSeitenzahl
520 (Printausgabe)
Dateigröße
31185 KB
Sprache
Englisch
EAN
9781638350910
Apply cutting-edge machine learning techniquesfrom 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.
Inside you'll learn modern, data-science-driven search techniques like:
Foreword by Grant Ingersoll.
About the technology
Modern 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.
About the book
AI-Powered Search 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).
What's inside
For software developers and data scientists familiar with the basics of search engine technology.
About the author
Trey Grainger is the Founder of Searchkernel and former Chief Algorithms Officer and SVP of Engineering at Lucidworks. Doug Turnbull is a Principal Engineer at Reddit and former Staff Relevance Engineer at Spotify. Max Irwin is the Founder of Max.io and former Managing Consultant at OpenSource Connections.
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.
Inside you'll learn modern, data-science-driven search techniques like:
- Semantic search using dense vector embeddings from foundation models
- Retrieval augmented generation (RAG)
- Question answering and summarization combining search and LLMs
- Fine-tuning transformer-based LLMs
- Personalized search based on user signals and vector embeddings
- Collecting user behavioral signals and building signals boosting models
- Semantic knowledge graphs for domain-specific learning
- Semantic query parsing, query-sense disambiguation, and query intent classification
- Implementing machine-learned ranking models (Learning to Rank)
- Building click models to automate machine-learned ranking
- Generative search, hybrid search, multimodal search, and the search frontier
Foreword by Grant Ingersoll.
About the technology
Modern 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.
About the book
AI-Powered Search 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).
What's inside
- Sparse lexical and embedding-based semantic search
- Question answering, RAG, and summarization using LLMs
- Personalized search and signals boosting models
- Learning to Rank, multimodal, and hybrid search
For software developers and data scientists familiar with the basics of search engine technology.
About the author
Trey Grainger is the Founder of Searchkernel and former Chief Algorithms Officer and SVP of Engineering at Lucidworks. Doug Turnbull is a Principal Engineer at Reddit and former Staff Relevance Engineer at Spotify. Max Irwin is the Founder of Max.io and former Managing Consultant at OpenSource Connections.
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