The exponential growth of data over the past decade has created new challenges in transforming raw information into actionable knowledge, particularly through the development of data products. The latter is essentially the result of querying and retrieving specific portions of data from a data storage architecture at various levels of granularity. Traditionally, this transformation depends on domain experts manually analyzing datasets and providing feedback to effectively describe or annotate data that facilitates data retrieval. Nevertheless, this is a very time-consuming process that highlights the need for its potential automation. To address this challenge, the present paper proposes a framework which utilizes Large Language Models to support data product discovery through semantic metadata reasoning and executable query prototyping. The framework is evaluated across two domains and three levels of concept complexity to assess the LLM’s ability to identify relevant datasets and generate executable data product queries under varying analytical demands. The findings indicate that LLMs perform effectively in simpler scenarios, but their performance declines as conceptual complexity and dataset volume increase.