#  Introduction   Large language models (LLMs) have a taste for using "flowery", sometimes overly verbose language in their responses. Ask a simple question, and chances are you may get flooded with paragraphs of overly detailed, enthusiastic, and complex prose. This usual behavior is rooted in their training, as they are optimized to be as helpful and conversational as possible. Unfortunately, verbosity is a serious aspect to have under the radar, and can be argued to often correlate with an increased odds of a major issue: hallucinations . The more words are generated in a response, the higher the chances of drifting from grounded knowledge and venturing into "the art of fabrication". In sum, robust guardrails are needed to prevent this double-sided problem, starting with verbosity checks. This article shows how to use the Textstat Python library to measure readability and detect overly complex responses before they reach the end user, forcing the model to refine its response.…