AI for Content Creation: Why Texts Converge to a Single Template and How to Measure It

Modern generative AI models increasingly produce content that feels monotonous and formulaic. This article explores the causes of this phenomenon, linked to the training specifics of language models and next-token prediction mechanisms. The author analyzes why algorithms tend toward average and predictable results, leading to a loss of text uniqueness. To evaluate this issue, the article proposes using technical data analysis methods, including the shingling algorithm, the MinHash method for set similarity, and structural analysis metrics. These tools allow for the quantitative measurement of content 'cliché-ness' and help identify patterns that make texts resemble one another. This material will be useful for developers and content managers aiming to improve the quality and originality of AI-generated content, as well as those seeking to understand the limitations of modern LLMs regarding creative variability.