When a Wine List Becomes a Logic Puzzle

Last week I went to a newly opened speakeasy and ordered a glass of white wine. The server who was young, friendly, but clearly new, admitted he hadn’t tasted any of the wines yet and neither had the bartenders, so they couldn’t tell me which ones were dry. I took a gamble on one with "blanc" in the name, chose poorly, and spent the evening with a glass of something I strongly disliked.

Tonight, faced with another wine list and determined not to repeat the mistake, I ran a small GenAI experiment: could ChatGPT help me predict which of the white wines would be the driest just from the menu?

I snapped a quick photo of the list and asked. The model broke down each varietal by typical dryness levels, explaining why Sauvignon Blanc and Albariño lean bone-dry, why Vinho Verde is dry but softer, and why Riesling labeled Spätlese is always going to skew sweet. It also flagged that Vouvray can be dry or off-dry (an important nuance I wish last week’s glass had come with).

What I appreciated most wasn’t just the answer but speed, clarity and the reasoning behind it. Instead of guessing, I understood why certain grapes, regions, and styles are predictably dry. The info was structured, had just enough detail for me to grasp quickly and made a solid recommendation. I double-checked with the bartender, who confirmed the guidance, and I ended up with a wine I genuinely liked.

This is the kind of everyday decision where GenAI shines: no drama, no over-engineering, just quick context that upgrades a small but meaningful choice. If last week’s experience was a reminder that “trial and error” can be unnecessarily costly (in both dollars and palate pain), tonight proved that a little well-timed intelligence can make even a simple glass of wine a lot better.

Tools Used

Tool: ChatGPT - Where to Find It: chat.openai.com - Cost: Free tier available; Plus subscription ($20/month) for enhanced features