What is the main reason a detailed prompt for summarizing news stories may not work?

A detailed prompt for summarizing news stories may not work primarily because large language models (LLMs) generally have a fixed knowledge cutoff date. In this scenario, if the model was last trained on data up until a certain point (in this case, October 2023), it will not have access to events or news occurring after that date. As a result, any requests for summarizing current news stories could yield outdated or irrelevant information, leading to ineffective or inaccurate summaries.

The other choices, while relevant to challenges one might face when using LLMs, do not specifically address the critical issue of timely information access inherent in news summarization. Lack of context about the type of newsletter may affect the relevance of the summarized content but does not fundamentally limit the model's ability to process current events. Output length being too long may create practical constraints; however, this scenario primarily emphasizes the inability to capture recent developments due to the knowledge cutoff. Lastly, while structured data can be a challenge in certain contexts, summarizing news stories typically doesn't rely on structured data formats, making it less relevant to the main issue presented.