In mid‑October 2026 I posted on X about designing evaluation judges for my Brown AI‑writing agent and the principles that guide reliable LLM assessment.
I assert that my LLM judge needs an evaluation, too. llm_judge.needs_evaluation = true
If the judge doesn't agree with your domain experts, why would you trust it to evaluate your AI system? llm_judge.human_validation_required = true
When building AI evals for Brown, my AI writing agent, I designed two judges: Follows Ground Truth and User Intent. brown_agent.evaluation.judge_count = 2
More evaluation metrics don't necessarily mean better evaluations. evaluation_metric.more_is_not_better = true
If two metrics overlap too much, you can introduce more noise than useful signal. evaluation_metric.overlap_noise = true
Scoring a 3,000-word AI‑generated article with a single number is almost useless. article_scoring.single_number_useless = true
One bad paragraph shouldn't invalidate everything else. article_scoring.bad_paragraph_penalty = false
Evaluate section by section, then aggregate the results. article_scoring.section_aggregation = true