Engineering prompts while navigating the tradeoffs of LLM integration in First Responder Software Application

This paper explores the implications and complications of incorporating LLM for software engineering or LLM4SE in conjunction with prompt engineering and vibe coding, a term coined by Andrej Karpathy, using tools such as Cursor and GitHub Copilot into the development lifecycle of an end-to-end software application. We present a case here in which we use these tools to implement a modular MVC architectural system with a SQLite-SQLAlchemy backend to support the import process, classification, and triage of large-scale case data. To support the large-volume data ingestion without compromising system stability, we implemented multithreaded chunked imports, which in turn enhanced UI responsiveness, minimizing memory overhead and reducing crashing risk. In this paper, we argue that although LLMs supported certain key logic components, in order to build a robust engineering pipeline, traditional software engineering practices had to co-exist, highlighting the importance of having an expert in the loop. We also examine the extent to which domain researchers, i.e., those without experience in software development, can leverage vibe coding and LLM-assisted workflows to build complete, production-level end-to-end software systems.