
LLM evaluation
Your LLM-As-A-Judge Might Be Lying to You
Detect positional bias and ranking cycles in pairwise LLM judges, then use balanced comparisons to improve evaluator reliability.
4 min read
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Detect positional bias and ranking cycles in pairwise LLM judges, then use balanced comparisons to improve evaluator reliability.

Learn why pairwise ranking gives more useful LLM evaluation results than numeric scoring, and how to infer relative quality.

Use a generate, evaluate, and revise loop to improve AI output more reliably than repeated prompt changes alone.

Use the RED framework to make binary LLM evaluations explain their reasoning, cite evidence, and return a clear decision.