Pinterest has improved its search relevance by implementing a large language model (LLM)-based pipeline that enhances how search queries align with Pins. The system utilizes knowledge distillation to scale a student relevance model from a teacher model, integrating enriched text features and conducting extensive offline and online experiments to validate its effectiveness. Results indicate significant improvements in search feed relevance and fulfillment rates across diverse languages and regions.

Celebrating two years at Weaviate, the author reflects on key insights about vector databases, emphasizing the importance of starting with traditional keyword search, understanding the nuances of vector search, and recognizing the interplay between vector databases and large language models. The article also addresses common misconceptions and offers practical advice on embedding models and search strategies.