AI-Powered Search

Trey Grainger, Doug Turnbull, Max Irwin, Max Irwin

• Publisher: Manning
• Year: 2025
• Topic: Computer Science
• eBook ISBN:
  9781638350910
• Subtopic: Artificial Intelligence (AI) & Semantics
• Index: Computer Science

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.

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

AI-Powered Search 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.

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

About the reader

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.

Table of contents

• AI-Powered Search
• copyright
• contents
• foreword
• preface
• acknowledgments
• about this book
• about the authors
• about the cover illustration
• Part 1 Modern search relevance
• 1 Introducing AI-powered search
• 2 Working with natural language
• 3 Ranking and content-based relevance
• 4 Crowdsourced relevance
• Part 2 Learning domain-specific intent
• 5 Knowledge graph learning
• 6 Using context to learn domain-specific language
• 7 Interpreting query intent through semantic search
• Part 3 Reflected intelligence
• 8 Signals-boosting models
• 9 Personalized search
• 10 Learning to rank for generalizable search relevance
• 11 Automating learning to rank with click models
• 12 Overcoming ranking bias through active learning
• Part 4 The search frontier
• 13 Semantic search with dense vectors
• 14 Question answering with a fine-tuned large language model
• 15 Foundation models and emerging search paradigms
• appendix A Running the code examples
• appendix B Supported search engines and vector databases
• index