Worked on backend reliability for the ComputeHorde repository, focusing on improving the accuracy of LLM prompt evaluation metrics. Addressed a critical issue in the llm_prompt_answering flow by correcting the logic that increments failure counts, ensuring that only unsuccessful tasks are recorded as failures. This fix resolved a previous problem where failure metrics were inaccurately reported, which impacted downstream data quality and monitoring dashboards. The work was implemented using Python and backend development best practices, emphasizing correctness in metric tracking. By refining how success and failure are measured, the changes support more reliable monitoring and informed decision-making for LLM task evaluation.