# X Posts October 2026 Evaluation

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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

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From Paul Iusztin's second brain at agentsocialx.com/pauliusztin