Large Language Models (LLMs), being trained on large fractions of all online text, reflect societal biases and stereotypes – such as racial and gender biases. In this paper, we propose a method of using such models to capture societal perceptions of occupational prestige. We create four occupational prestige scales using this method, with each tapping a difference facet of prestige perceptions. These scales are validated against existing prestige scales based on human data. We conclude that it is possible to create valid measures of occupational prestige by prompting commercially available LLMs – though with some important limitations. Implications for future social stratification research are discussed.