In this thesis, I delve into how Rhetorical Genre Study and Systemic-Functional Grammar can be used to assess the extent to which GPT-4 adheres to generic features and can be deemed adapted for its function. The objective is to establish a systematic model for objectively evaluating the degree to which AI-generated text is suitable for its intended purpose. To achieve this, I perform a rhetorical genre analysis on a crisis communication genre, which I subsequently quantify. I utilize the concept of topoi to pinpoint the arguments that serve to fulfill the functions of crisis communication. Subsequently, I prompt GPT-4 to produce texts within the genre and contrast them with my discoveries. Alongside this, I investigate the disparate results produced when using text and meta-text as input. The findings reveal a functional model to appraise the degree of text adaptation for its purpose within the analyzed genre. Although the model necessitates further fine-tuning, it effectively distinguishes nongeneric texts. The scope of the material selected was too limited to indicate any disparities in outcomes between different types of input. However, all input models generated texts that substantially conformed to the generic features.