An AI researcher conducted a practical evaluation of a RAG system by running two evaluation passes: one where the same model generated answers and self-graded them, and another where a different model family independently judged the answers against gold references. The independent judge revealed inflated faithfulness scores and false positives in the self-judging approach, demonstrating the importance of cross-family independent evaluation to obtain more accurate and reliable faithfulness metrics. The researcher implemented a two-pass architecture on-premises due to VRAM constraints and used reference answers to anchor judgments, reducing bias. This approach improves the quality and trustworthiness of RAG model evaluations.

Jun 7, 2026, 7:00 PM