Bofeng Huang developed a rubric-driven GEval scoring normalization feature for the confident-ai/deepeval repository, focusing on backend development and metric implementation using Python. He introduced dynamic score range calculation and strict mode support, allowing the system to accurately normalize scores based on rubric definitions. Through careful code refactoring, he resolved critical issues in score range handling, which improved metric accuracy and enabled reliable cross-rubric comparisons. His work resulted in cleaner data normalization and reduced the need for manual adjustments in evaluation pipelines. The changes were well-documented with clear commit history, reflecting maintainable engineering practices and attention to code quality.