Optimizing Enterprise RAG Systems

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.