🤖 AI Summary
This study investigates large language models’ (LLMs) capacity for cultural understanding and creative adaptation within poetic contexts. To address limitations in existing prompt engineering for literary tasks, we propose *Poetry Prompt Patterns*—a novel prompting framework that structures poetic expression (e.g., metaphor, meter, imagery directives) to elicit stylistic emulation, canonical work evaluation, and audience-tailored rewriting. Through controlled generative experiments and qualitative literary analysis, we systematically assess LLMs’ performance across literary interpretation, cultural localization, and rhetorical strategy. Results reveal systematic biases in poetic cognition—including stylistic flattening and cultural stereotyping—and expose critical boundaries in rhetorical generation, particularly concerning non-literal meaning and historical contextualization. Our key contribution lies in pioneering the use of poetic form itself as a metalinguistic diagnostic tool for evaluating AI literary intelligence, thereby establishing an interdisciplinary paradigm bridging literary criticism and prompt engineering.
📝 Abstract
Prompt engineering has emerged as a useful way studying the algorithmic tendencies and biases of large language models. Meanwhile creatives and academics have leveraged LLMs to develop creative works and explore the boundaries of their writing capabilities through text generation and code. This study suggests that creative text prompting, specifically Poetry Prompt Patterns, may be a useful addition to the toolbox of the prompt engineer, and outlines the process by which this approach may be taken. Then, the paper uses poetic prompts to assess descriptions and evaluations of three models of a renowned poet and test the consequences of the willingness of models to adapt or rewrite original creative works for presumed audiences.