## 4 Discussion
### Limitations  
It is important to note some of the limitations of our study. The demographics, geographical location, and even previous daily activities of the authors of text-based data can result in substantially different word choices even when discussing the same subject matter. While it is beyond the scope of this work to control for these biases, it is important to note that they exist and could influence the analysis that informs this article. Geographic bias that influences the sentiment analysis of text-based social media messages has been highlighted in [88; 89]. Another study shows that the sentiment of social media about a given subject can be biased by the time of day the message is authored and if the author is a resident or visitor in the city in which the message is composed [90]. Additionally, the reliability and validity of targeted narrative messaging, such as the narrator-subject relationship utilized in our prompt structure, needs further evaluation with specific attention to the context of the simulation setting to gauge how well the message serves as a true representation of the original life event. 1. **Problem type. ** For this study, ChatGPT was not required to solve complex problems or rely heavily on factual information from training data. All the required factual information, including narrator and subject characteristics, was provided in the prompt. ChatGPT appears well suited to this style of creative task and provides technically correct outputs in a majority of instances as long as the instructions and constraints in the input prompt are observed. Narratives can easily be validated manually by comparing prompt inputs to the created narratives. This differs from other types of tasks, such as asking ChatGPT to solve a mathematical problem or to diagnose a medical condition [28], which require domain knowledge of much more complex background information that is not included in the prompt. These problem types are much less subjective and not as easily validated. Further, ChatGPT currently cannot accurately provide sources or references for validating the response information. In this case, the human reader has to determine if the response is legitimate or if ChatGPT has \"hallucinated\" some trustworthy-sounding but incorrect response, without the benefit of reliable references [36]. 2. **Use case. ** For this study, the generated narratives were not posted to social media or broadcast in any way, but were used solely for analysis. Incorrect narratives were identified manually and used to inform corrections to the structured narrative prompt but did not incur any other negative consequences. For use cases in which responses are not or cannot be validated by a human before utilization, there is a risk of dissemination of erroneous information. Numerous correct prior responses do not prevent incorrect responses from occurring in the future; in other words, there is no way to bound or know the response space [36]. As noted in the second lesson learned, even with minimal stochasticity, ChatGPT generated completely incorrect narratives about car fires and house fires, which could not have been predicted by the hundreds of preceding responses for that event type which did not do this. 3. **ChatGPT API response speed. ** As noted in the third lesson learned, the Python API regularly failed due to time-out errors, so this currently might not be an appropriate tool for situations with strict time constraints. 4. **Token volume in real time/quicker than real time. ** The ability to generalize our approach for real-time applications of ChatGPT for narrative generation is limited based on the token limit of the API. The current ChatGPT API version has a rate limit of 3500 requests per minute and 90,000 tokens per minute [91]. 5. **Domain expertise. ** The creation of LLMs is based on broad ranges of volumes of reference literature. It is important to determine that generated results are in line with the domain expertise of the targeted problem or system [92].