Ranking systems have traditionally been built around carefully engineered objective functions, learned behavior, and years of optimization. But what happens when we introduce models that can reason, interpret intent, and understand natural language? This talk explores how LLMs are reshaping modern ranking systems through the lens of engineering tradeoffs. Drawing on the latest academic research and public industry work, we'll examine emerging approaches to expressing ranking objectives, combining reasoning with prediction, designing production-ready architectures, and tackling new challenges around validation and continuous improvement.
Rather than focusing on a single architecture, this session explores what researchers and industry practitioners are trying today, what seems promising, and what questions remain open.
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