In this talk, we introduce “Learning to Understand” as a corollary to the well known “Learning to Rank” process for building ranking classifiers. BM25, Dense Vector Search, and even Hybrid Search approaches all share one thing in common: they focus primarily on algorithmic ranking of search results, as opposed to query understanding for first identifying and matching the right set of potential query interpretations.

Ranking search results is important, but in many cases implementing a query understanding and rewriting layer prior to executing a query provides substantially more value. By identifying the right query interpretation upfront, you can better avoid false positives, better identify multiple interpretations of ambiguous queries, and better target your actual domain-specific terminology and dataset.

We’ll walk through how to properly segment the “query understanding” phase from the “matching and ranking” phase, as well as how to use evals to learn domain-specific query patterns and integrate tools like semantic knowledge graphs to generate high-quality, in-domain query understanding prior to rewriting and executing a much more intelligent query for matching and ranking. By using evals to learn domain-specific query interpretation rules and combining them with semantic statistics from your index, it’s possible to significantly improve search quality beyond typical BM25, vector, and hybrid search techniques.