Worked on enhancing the reliability of the JSON loading process in the confident-ai/deepeval repository by addressing error handling when large language model outputs are unexpected. Focused on the trimAndLoadJson function, introducing explicit checks for None input to prevent AttributeError and ensure graceful failure with clear, actionable error messages. Leveraged Python for robust data processing and implemented comprehensive test coverage to validate the new error handling logic. These improvements reinforced the stability of the data pipeline, reduced the risk of runtime crashes, and improved downstream metric reliability, contributing to more dependable processing of LLM-generated data within the project.