Engineering a LLM-Based Data Ingestion Process for Semi-Structured Documents: An Empirical Comparison with Human-Centered Workflows - Coutinho, Sandrelly L.; Adeodato, Paulo J. L.; Souza, Bruno C. de; Fontes, Carlos E. de S. (Anais do Congresso Ibero-Americano em Engenharia de Software (CIbSE); 2026: Anais do XXIX Congresso Ibero-Americano em Engenharia de Software, 2026)
Data-driven software systems increasingly rely on ingesting information from semi-structured documents, whose manual processing is costly and error-prone. Although Large Language Models (LLMs) can interpret such documents, their systematic integration into reliable ingestion processes remains underexplored. This paper proposes an LLM-driven data ingestion process from a Software Engineering perspective, distinguishing prompt-based optimization from model training. Prompts are treated as versioned engineering artifacts and refined iteratively through automated comparison with ground truth under stateless execution. A controlled experiment evaluates multiple LLM backends using extraction quality, cost, and generalization.