Worked on stabilizing the caching mechanism in the BerriAI/litellm repository by addressing a TypeError in the get_cache_key function. The solution involved filtering the preset_cache_key from keyword arguments before passing them downstream, which prevented multiple values errors and improved the reliability of cache key computation. Added comprehensive unit tests to cover the bug scenario, regression paths, and the handling of litellm_params, thereby reducing production risk. Utilized Python for backend development, focusing on debugging and unit testing to enhance system stability. The work resulted in more robust caching behavior and minimized runtime errors, contributing to improved downstream reliability.