2 Years of ML vs. 1 Month of Prompting

A major automaker's analytics team spent two years building a supervised machine learning system to automatically classify vehicle warranty claims, requiring months of manual data labeling by experts and complex preprocessing pipelines. When they tested modern large language models as an alternative, initial results showed the traditional XGBoost model outperformed LLMs by about 15%. However, after just six rounds of prompt refinement over one month, the Nova Lite model matched or exceeded their supervised system's performance in four out of five categories, demonstrating how LLMs can replace lengthy data annotation cycles with rapid prompt iteration.