AI-Powered Search

• 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.