Large language models are increasingly evaluated using automated graders that output scalar scores or preferences, but these approaches lack interpretability as a single score cannot explain why an answer is good or bad. Rubric-based evaluation offers a more transparent alternative by decomposing quality into explicit, checkable criteria. However, manually designing high-quality, query-specific rubrics is labor-intensive and cognitively demanding. This work investigates whether LLMs can generate interpretable and effective rubrics through domain knowledge retrieval for automated evaluation. The proposed RubricRAG framework aims to enhance the interpretability and reliability of LLM evaluation by leveraging retrieved domain knowledge to generate task-specific evaluation rubrics.