Making Hotel Policy Search Understand Real Guest Questions

Hotels face a persistent gap between how guests naturally ask questions and how standard retrieval systems work. This article explores a semantic embedding approach with a self-healing loop that prioritizes surfacing weak matches and enabling recovery through synonym expansion and evidence bundling, rather than chasing perfect scores on toy datasets. The principle applies broadly to customer-facing systems where natural language significantly diverges from procedural documentation.