<p>Most current LLM applications in social science are measurement tasks, but they are rarely treated systematically within an explicit measurement framework. The field has largely used LLMs as drop-in replacements for human coders at a single stage of the conventional measurement pipeline, without asking whether a pipeline built around modular human judgment and downstream statistical modeling is still the right architecture. This paper takes a perspective from measurement theory, mapping existing applications onto the classical measurement pipeline and showing that most studies simply swap an LLM in for a human coder at one stage, without asking how a fundamentally different kind of instrument might affect the pipeline itself. We then develop a taxonomy of LLM-based measurement strategies organized by degree of integration into the pipeline, and identify areas where evaluating LLM-based measures requires attention that differs fundamentally from conventional practice. We treat LLM outputs not as automatic measurements but as candidate measures of a distinctive kind. The aim is not to provide a single workflow but to identify the issues that must be seriously considered when using LLMs for measurement.</p>