In today's e-commerce landscape, hybrid search systems represent the market standard, yet they often struggle with highly inspirational queries, lack of precision in semantic recall, and presentation of diversity. Ambiguity in search terms can lead to disorganized results or overshadowed relevant products.

This session will demonstrate how an upfront LLM-supported in depth query understanding can increase recall meaningfully without sacrificing precision. We show how LLM-driven query understanding can enhance retrieval even for highly inspirational searches and how a final alignment between retrieved products, the query intent and the query context can compensate for indexing shortcomings. Additionally, we'll explore how query intent-based clustering can visually organize results by disambiguating meanings, thus providing users with a clearer path to relevant products.

Our discussion will take attendees through our journey from prototype to production-ready implementation, sharing insights and challenges encountered in large-scale e-commerce environments with high query volume.

Intent-based clustering represents a novel approach in e-commerce, reducing the "paradox of choice" and aiding customers in navigating product diversity. We'll explore various presentation forms for query intent-based clusters and share findings from UX tests that evaluated these approaches.

Join us to gain a comprehensive understanding of how these advanced techniques can transform search experiences and drive better outcomes in e-commerce settings.