Etsy utilizes large language models (LLMs) to create detailed, anonymous buyer profiles based on browsing and purchasing history. These profiles capture nuanced interests and shopping missions, enhancing personalization for nearly 90 million buyers on the platform. The technical implementation involves retrieving user activity data and then prompting an LLM to interpret this data for profile generation. To make this process scalable and cost-effective, Etsy optimized data sources, reduced input token volume, increased batch sizes, and employed parallel processing. These optimizations drastically reduced buyer profile generation time and costs. The generated buyer profiles are then applied to personalize the search experience through query rewriting and refinement pills. Query rewriting enriches user searches with predicted interests, while refinement pills offer clickable filters based on user preferences. Etsy measures the success of this personalization through metrics like click-through rate and conversion rate lifts. They also maintain profile accuracy by dynamically refreshing them based on user activity and detecting interest drift. Future work includes addressing the "cold start" problem for new users by experimenting with inheritance profiles. Ultimately, Etsy aims to improve discovery and create more intuitive search experiences for every shopper.