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A developer built a multi-model evaluation panel where several large language models (LLMs) judged candidate outputs to select the best answer. Initially, the panel consistently favored outputs that matched the judges' own model style, demonstrating self-preference bias. To fix this, the developer implemented an anonymized peer review system (llm-council by Andrej Karpathy) that hides the identity of each candidate output from the judging models, labeling them neutrally (e.g., Response A, Response B). This removal of identity information eliminated self-preference bias, resulting in more diverse and quality-focused selections. The panel aggregates rankings by averaging rank positions to select winners. However, other biases like verbosity bias and position bias remain and require additional mitigation.