Etsy is using large language models (LLMs) to create detailed, anonymous buyer profiles to enhance personalization for its nearly 90 million users. These profiles capture nuanced interests like preferred styles and product categories based on browsing and purchasing behaviors. The technical implementation involves retrieving user activity data and prompting the LLM to interpret this data and generate structured profiles. Etsy optimized the profile generation process by streamlining data sources, reducing input tokens, and increasing batch sizes, significantly reducing generation time and costs. These buyer profiles are being used in applications such as query rewriting and refinement pills to improve search relevance and user experience. Etsy is measuring the success of these profiles by tracking metrics like click-through rate, conversion rate, and refinement pill engagement. They also plan to address the "cold start" problem for new users by using collaborative filtering to match them with similar existing profiles. Etsy is actively monitoring profile accuracy and adjusting refresh strategies based on user activity and seasonal considerations. This personalization enhances buyer experience by surfacing items tailored to individual tastes.