The advent of large language models (LLMs) presents a promising opportunity for how we analyze text and, by extension, can study the role of culture and symbolic meanings in social life. Using an illustrative example focused on the concept of “personalized service” within Michelin-starred restaurants, this research note demonstrates how LLMs can reliably identify complex, multifaceted concepts similarly to a qualitative data analyst, but in a more scalable manner. We extend existing validation approaches, offering guidelines on the amount of manually coded data needed to evaluate LLM-generated outputs, drawing on sampling theory and a data simulation. We also discuss broader applications of LLMs in cultural sociology, such as investigations on established concepts (e.g., cultural consecration) and emerging concepts (e.g., future-oriented deliberation). This discussion underscores that AI-tools can significantly augment the empirical scope of research projects, building on rather than replacing traditional qualitative approaches. Our study ultimately advocates for an optimistic yet cautious engagement with AI-tools in social scientific inquiry, highlighting both their analytic potential and the need for ongoing reflection on their ethical implications.
