Aleksandr Goncharov focused on backend reliability for the ComputeHorde repository, addressing a critical issue in the evaluation of large language model prompt metrics. He identified and corrected the logic governing failure metric increments within the llm_prompt_answering flow, ensuring that failure counts now accurately reflect only unsuccessful tasks. This Python-based solution improved the integrity of monitoring data, which is essential for downstream dashboards and analytics. Aleksandr’s work centered on backend development, emphasizing correctness in data collection and reporting. Although the scope was limited to a single bug fix, the depth of analysis and targeted resolution enhanced the quality of LLM task evaluation.