## 5 Conclusions  
This study provides a structured narrative prompt for consistently and transparently providing information to LLM platforms in order to yield narratives oriented around specific types of life events. Experimentation with the structured narrative prompt using OpenAI's ChatGPT-3.5 API showed that correct narratives can be generated with sentiment levels that are indiscernible from the sentiment of real tweets. This supports that the structured narrative prompt can serve as a promising avenue for generating sentimentally realistic narratives of simulated agents. Assessments for statistically significant differences in sentiment scores between ChatGPT-generated narratives and real tweets are generated using chi-Squared tests. Overall, four of the forty-four tested comparisons using Birth, Death, Hired, and Fired themed life events were found to be statistically indiscernible from real tweets. In 32 of the 44 comparisons, ChatGPT-generated narratives achieved higher average sentiment scores across the 11 PANAS groups. Additionally, the results clearly show that the sentiment levels conveyed alongside the ChatGPT-generated narratives varied widely across the four utilized event types.  
Trends were observed tending towards higher mean sentiment of positive traits within the ChatGPT-generated narratives. Whether this tendency holds for other LLM platforms is not something that can be generalized from this study and research into the sentiment tendencies of other LLMs requires separate evaluation. Based on the iterative development process for developing the LLM structured narrative prompt, target sentiment level is already a primary input field (field 11); however, additional research is required to further calibrate the desired sentiment level for a given context or event type to potentially scale back the overall sentiment being generated within the created narratives.  
Our study relates to desires expressed by the medical community for formulaic LLM prompts [39]. Our findings suggest that using a similar structured prompt can be helpful in obtaining useful LLM responses in critical settings such as healthcare, clinical training,or risk communication. Our results are but a single data point in this emerging and quickly expanding field. However, researchers should conduct additional domain-specific research into the appropriateness of the LLM structured narrative prompt for other domains.  
Conceptualization, C.J.L., E.J.J., V.Z., K.O., E.F. and R.G.; methodology, C.J.L., E.J.J. and R.G.; software, C.J.L., R.G. and E.J.J.; validation, C.J.L., R.G. and E.J.J.; formal analysis, C.J.L., R.G. and E.J.J.; investigation, [PERSON], R.G. and E.J.J.; data curation, [PERSON], R.G. and E.J.J.; writing--original draft preparation, [PERSON], E.J.J. and R.G.; writing--review and editing, [PERSON], E.J., V.Z., K.O., E.F. and R.G.; visualization, [PERSON], E.J.J. and R.G.; funding acquisition, R.G. and C.J.L. All authors have read and agreed to the published version of the manuscript.  
This research was supported, in part, by Old Dominion University through project 300916-010, Effective & Individualized Risk Communication [74].  
Not applicable as this research did not involve the use of human subjects. However, the previously constructed Twitter dataset was approved as EXEMPT FROM IRB REVIEW by the Old Dominion University Institutional Review Board on 13 May 2022 per Exemption category #2 from federal regulations [43].  
Not applicable.  
All the data, code, and generated narratives utilized in this article are freely accessible in an online repository [86].  
The authors declare no conflict of interest.  
The following abbreviations are used in this manuscript:  
ABM Agent-Based Model API Application Programming Interface GPT Generative Pre-trained Transformer LLM Large Language Model PANAS Positive and Negative Affect Schedule