Direct recommendation rates are evaluated by prompting AI models with strict binary questions—asking if they would recommend a brand for a specific query or entity—across repeated sampled iterations to eliminate stochastic variance. The proportion of affirmative answers is aggregated into a Query Relevance Score (QRS), pinpointing topic-specific relevance gaps where a model hesitates to endorse the brand.