AI-Powered Search (MEAP V15)

Build search engines powered by the latest machine learning techniques and large language models.

AI-Powered Search shows you how to build cutting-edge search engines that continuously learn from both your users and your content and drive more domain-aware and intelligent search.

Inside you’ll learn modern, data-science-driven search techniques like:

• Semantic search using dense vector embeddings from foundation models
• Retrieval augmented generation
• 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
• Implementing machine-learned ranking models (learning to rank)
• Building click models to automate machine-learned ranking
• Generative search, hybrid search, and the search frontier

Today’s search engines are expected to be smart, understanding the nuances of natural language queries, as well as each user’s 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. You’ll 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 book

AI-Powered Search is a hands-on guide to applying leading-edge 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.

You’ll 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, you’ll deploy intelligent AI-powered search systems that deliver real-time personalization and contextual understanding of each user, domain, and query through a self-learning search platform that continuously learns from evolving content and user interactions.