Jev turns 2,000 wine reviews into 67 numeric features for CatBoost
In a TypeSafe cookbook workflow, an LLM posed semantic questions about each review and Jev answered them to create feature columns, cutting RMSE from 3.09 to 1.77 after five rounds.
JEV TURNED 2,000 WINE REVIEWS INTO 67 NUMERIC COLUMNS THEN A BASIC ML MODEL CUT ITS ERROR ALMOST IN HALF this is the Jev demo people should be talking about the system did not ask Jev to write better tasting notes. An LLM proposed semantic questions about each review. Jev answered those questions across the dataset. The answers became numeric features: expected scores, probabilities and uncertainty then CatBoost did the final prediction the mean baseline started at 3.09 RMSE. Word-count features reached 2.47. Asking Jev directly for the score reached 2.15. But after five rounds of text-to-feature discovery, CatBoost reached 1.77 that result comes from TypeSafe's own cookbook, not an independent benchmark. Still, the architecture is the important part: LLM discovers concepts Jev measures them Classical ML learns what matters the next AI primitive may not be another chatbot it may be a machine that turns language into columns software can actually use
This post quotes another post: x.com/fluixoo/status/2103123493567025321