An O’Reilly Radar essay examines a familiar LLM writing tic the author calls “Yoda-speak”: sentences whose parts are reordered and strung together with commas, producing syntax that reads like Yoda. He argues it follows from autoregressive generation — a model writes one word at a time and cannot go back to reorder what it has already committed, so late-arriving details get appended to an already awkward sentence, the same limitation he sees behind models that offer a “better version” instead of editing the original. He cites a 2024 paper by Wilson Wu, John X. Morris and Lionel Levine, “Do language models plan ahead for future tokens?”, which found evidence that models do prepare information for later tokens, with the effect growing in larger models — though, he notes, anticipating is not revising. The author also describes an experiment of his own, but the source text is truncated before its results.