Mise en Place for Taste: Recipes, Connoisseurship, and Cultural Competence in Generative AI

Monet did not cook. The recipes in The Monet Cookbook were prepared by a household kitchen the painter hosted, sourced for, and appreciated but did not author. We argue this gap is a precise analogy for contemporary cultural AI evaluation, which has inherited a 19th-century specification thesis: that cultural competence can be written down and transmitted at scale. The French gastronomic literature from Brillat-Savarin through Babinski records the thesis's structural failure mode: connoisseurship (trained, indexical, relational, untransferable judgment) cannot be specified, only deferred to a competent agent at execution time. The cookbook genre marks these positions with the phrase to taste. We propose taking this primitive seriously and reorganizing evaluation around a paradigm we call mise en place for taste: targeting the conditions under which competent judgment can arise, rather than auditing outputs against rubrics. Four concrete proposals follow. By separating cooking from hosting, the argument also addresses the displacement risk that better cultural AI poses to working artists.