Soppia: A Structured Prompting Framework for the Proportional Assessment of Non-Pecuniary Damages in Personal Injury Cases

TL;DR

The paper addresses the challenge of quantifying non-pecuniary damages in personal injury by reducing subjective variability. It proposes Soppia, a structured prompting framework that encodes 12 Art. 223-G CLT criteria into an auditable AI-assisted workflow. It employs a calibrated dual-logic scoring, weighted aggregation, and four severity classes to produce proportionate compensation ranges, while maintaining human oversight. The work emphasizes transparency, reproducibility, and adaptability to different jurisdictions, aiming to improve fairness and predictability in legal decision-making.

Abstract

Applying complex legal rules characterized by multiple, heterogeneously weighted criteria presents a fundamental challenge in judicial decision-making, often hindering the consistent realization of legislative intent. This challenge is particularly evident in the quantification of non-pecuniary damages in personal injury cases. This paper introduces Soppia, a structured prompting framework designed to assist legal professionals in navigating this complexity. By leveraging advanced AI, the system ensures a comprehensive and balanced analysis of all stipulated criteria, fulfilling the legislator's intent that compensation be determined through a holistic assessment of each case. Using the twelve criteria for non-pecuniary damages established in the Brazilian CLT (Art. 223-G) as a case study, we demonstrate how Soppia (System for Ordered Proportional and Pondered Intelligent Assessment) operationalizes nuanced legal commands into a practical, replicable, and transparent methodology. The framework enhances consistency and predictability while providing a versatile and explainable tool adaptable across multi-criteria legal contexts, bridging normative interpretation and computational reasoning toward auditable legal AI.