Galen Topper developed a Faithfulness Testing Framework for the JudgmentLabs/judgeval repository, focusing on enhancing model evaluation capabilities. He engineered a new data pipeline by adding an is_hallucination column to cstone_data.csv, enabling quantitative assessment of response faithfulness across language models. The core implementation, faithfulness_testing.py, integrated Python libraries such as Patronus, Ragas, and JudgmentClient to automate the evaluation process. Galen’s work combined data analysis, data engineering, and LLM evaluation skills to lay a foundation for future comparative studies. The depth of the solution reflects a thoughtful approach to extensibility and automated testing, though completed within a single feature cycle.