Galen Topper developed a Faithfulness Testing Framework for the JudgmentLabs/judgeval repository, focusing on quantitative evaluation of language model responses. He engineered a new data pipeline by adding an is_hallucination column to cstone_data.csv, enabling systematic assessment of response faithfulness. Galen integrated Python-based tools, including Patronus, Ragas, and JudgmentClient, within a unified script to automate the evaluation process across multiple competitors. His work combined data analysis, data engineering, and LLM evaluation skills to lay the groundwork for future model comparisons. The project delivered a single, feature-rich commit, reflecting a focused and foundational contribution over the course of one month.