Large language models (LLMs) have transitioned from emerging to mainstream technology, significantly impacting natural language processing (NLP) applications. The text explores LLM use cases, focusing on search, clustering, and classification, which extend beyond mere text generation to include text representation, making it possible to process vast amounts of unstructured data. In search applications, LLMs enhance semantic similarity matching, enabling more context-aware and relevant search results. Clustering organizes documents into groups based on themes or topics, while classification categorizes text into predefined classes, demonstrating supervised learning. These capabilities facilitate a wide range of applications, including customer feedback organization, sentiment analysis, and content moderation, showcasing the extensive potential of LLMs in addressing NLP challenges with accessible AI technologies.