We explore a novel approach to reading compliance, leveraging large language models to select inline challenges that discourage skipping during reading. This lightweight ‘testing’ is accomplished through automatically identified context clozes where the reader must supply a missing word that would be hard to guess if earlier material was skipped. Clozes are selected by scoring each word by the contrast between its likelihood with and without prior sentences as context, preferring to leave gaps where this contrast is high. We report results of an initial human-participant test that indicates this method can find clozes that have this property.