PART 1 MODERN SEARCH RELEVANCE .......................................... 1
1 ■ Introducing AI-powered search 3
2 ■ Working with natural language 25
3 ■ Ranking and content-based relevance 49
4 ■ Crowdsourced relevance 78
PART 2 LEARNING DOMAIN-SPECIFIC INTENT ........................... 101
5 ■ Knowledge graph learning 103
6 ■ Using context to learn domain-specific language 131
7 ■ Interpreting query intent through semantic search 161
PART 3 REFLECTED INTELLIGENCE .......................................... 191
8 ■ Signals-boosting models 193
9 ■ Personalized search 216
10 ■ Learning to rank for generalizable search relevance 254
11 ■ Automating learning to rank with click models 285
12 ■ Overcoming ranking bias through active learning 314
PART 4 THE SEARCH FRONTIER ............................................... 339
13 ■ Semantic search with dense vectors 341
14 ■ Question answering with a fine-tuned large language model 396
15 ■ Foundation models and emerging search paradigms 426

 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.