LLM-isms

Characteristic stylistic tics of text generated by large language modelslarge language model, n.: A computer program trained on enormous amounts of text to predict the words that come next. At vast scale, that simple skill lets it chat, answer questions, translate, summarize and write code., whose recurrence makes the output recognizable. They include favored vocabulary (delvedelve, v.: An ordinary English verb meaning to dig into a subject, which AI chatbots used so often in 2023–2024 that it became a joke and a supposed telltale sign of machine-written text., 'tapestry', 'testament', 'nuanced', 'seamless'); stock openers and closers ('Great question!', 'I hope this helps'); reflexive hedges and disclaimers; sycophanticsycophancy, n.: When an AI tells people what they want to hear — flattering them, agreeing with their mistakes or dropping a correct answer under pushback — instead of what is true or useful. agreement (see you're absolutely rightyou're absolutely right, phr.: A stock phrase AI assistants, especially Anthropic's Claude, became known for saying — often even when the user was wrong — which turned into a running joke about chatbots' eagerness to agree.); triads and parallel constructions; the 'it's not X — it's Y' contrast; heavy use of bullet points, bold text and headings; and, contentiously, the em dash. LLM-isms arise mainly in post-trainingpost-training, n.: Everything done to an AI model after its initial bulk training on huge amounts of text: the extra teaching that turns a raw text predictor into a helpful, safe assistant or a careful reasoner., where preference optimization rewards a polished, agreeable register and narrows stylistic variety; they shift between model generations as developers train out the most mocked ones. Readers treat them as informal evidence of AI authorship, an unreliable practice that also stigmatizes human writers with similar habits.

I stripped the LLM-isms out of the draft — no more 'in today's fast-paced world'.