This study examined predictors of successful smoking cessation using data from waves 5 and 6 of the Population Assessment of Tobacco and Health study. Researchers used OpenAI’s GPT-4.1 to perform text-based feature selection by analyzing descriptions of survey variables, identifying 45 variables most predictive of 12-month smoking abstinence. These variables were then used to train an eXtreme Gradient Boosting model, which showed nearly identical predictive performance compared to a model trained using all available variables. The top predictors included smoking frequency, time to first cigarette after waking, social influences on tobacco use, emotional dependence, electronic nicotine product use, and health harm concerns. The findings demonstrate that large language models can efficiently identify key predictive variables using textual data alone.