This tutorial introduces a framework for classifying large language models used as variation operators in metaheuristics, organized around two descriptors: the type of prompt-conditioning information (Numeric, Symbolic, Linguistic) and artifact persistence (what survives the model call). It provides practitioners with tools to build, classify, and select LLM-based operators through templates, method surveys, and cost-aware decision guides.