Isabella Siu developed comprehensive zero-shot approach tutorial documentation for the Open-Finance-Lab/FinLLM-Leaderboard repository, focusing on FLARE-FIQASA datasets and the Llama-3.2-1B model. Her work detailed sentiment classification tasks, dataset usage, and model references, while also addressing partial matching evaluation and concurrency considerations to improve workflow efficiency. She incorporated token streaming techniques and performance logging, enabling reproducible analysis and optimization of zero-shot machine learning workflows. Using her expertise in documentation, machine learning, and natural language processing, Isabella produced user-facing materials that facilitate onboarding and provide engineers with clear, actionable guidance for analyzing and enhancing zero-shot evaluation pipelines.