The general buzz around AI is not fading away anytime soon. However, a technical understanding of these tools remains a complicated conversation. Large Language Models (LLMs) cannot understand and emulate human-like conversations. They are trained with huge volumes of data to give a particular output based on the specific input. But they lack the ability to comprehend the true meaning behind those words. Any response generated by LLMs will lack a basic understanding of the context.

Poetry and prose are written artistic expressions that help us to appreciate the reality we live. Each of these styles has its own set of subjective properties, such as rhyme and rhythm, which are easily caught by a human reader's eye and ear. With the recent advances in artificial intelligence, the gap between humans and machines may have decreased, and today we observe algorithms mastering tasks that were once exclusively performed by humans. In this paper, we propose an automated method to distinguish between poetry and prose based solely on aural and rhythmic properties. In other to compare prose and poetry rhythms, we represent the rhymes and phones as temporal sequences and thus we propose a procedure for extracting rhythmic features from these sequences.