In this episode is focussed an in-depth analysis of a Microsoft patent for a "deep search" system that leverages Large Language Models (LLMs) to refine search results beyond traditional ranking methods. The core process involves disambiguating a user's initial query to identify a "primary intent," using a second LLM to generate more focused "alternative queries," and then using a third LLM to score the resulting web pages for relevance against the clarified intent. This hybrid architecture signals an acknowledgement that traditional algorithms excel at recall (finding broad results) but require LLMs for semantic precision and intent-based ranking, especially for sensitive or complex topics where trustworthiness is given critical weighting. The process also suggests that content creation should shift from simple keyword optimization to intent optimization to achieve high scores in this new paradigm.https://www.kopp-online-marketing.com/patents-papers/deep-search-using-large-language-models