The intersection between Large Language Models (LLMs) and Search technologies is an exciting area with significant potential for improvement in both fields. Retrieval-Augmented Generation, Query Understanding, Index Construction, LLMs in Re-Ranking, and Search Result Compression are five key components of this intersection. LLMs can improve search capabilities by enabling language models to reason about new data without gradient descent optimization, making it easier to update information, attributing sources, reducing parameter count, and enhancing the ability to formulate search queries. Additionally, LLMs can transform information for building search indexes, rank search results with symbolic preferences, and generate personalized ads by linking outputs back to databases. Generative Feedback Loops is a term used to describe cases where the output of an LLM inference is saved back into the database for future use.