Resilient Guest-Policy Retrieval: A Self-Healing Semantic Loop for Hotel Context

How I Recovered Weak Matches with Controlled Expansion and Bundled Evidence in a Solo PoC TL;DR This write-up documents a personal experiment I ran while thinking about how guests actually ask questions at the front desk, on the phone, or in a chat widget. The phrasing is rarely canonical. Someone might say they want a rubdown later today when the policy text says massage appointments and cancellation windows. Another guest might describe late checkout as staying past the morning rush. If you treat every utterance as a perfect keyword match, you will look clever in a slide deck and brittle in real language. I built a small Python system that embeds synthetic hotel policy chunks with a compact sentence transformer, measures cosine similarity against guest questions, and applies a narrow healing loop when the score falls below a floor I set by hand. The loop tries controlled synonym expansion first, then merges the top passages into a bundled evidence string when the model still hesitate