#llmasjudge

An un-seeded LLM judge is a coin flip even at temperature 0. How we made signed eval verdicts replayable instead of attesting noise.

Using one AI to grade another is now common — but the biggest audit yet shows these graders are consistent without being correct. A judge that always picks "answer A" scores perfectly on consistency.

광고 카피 자동 생성·RAG 답변 품질·챗봇 응답 평가는 사람이 다 못 봅니다. LLM에게 "이 출력이 좋은가"를 물어 점수를 받는 LLM-as-judge가 표준이 되어가지만, 그 자체가 깨지는 자리도 많습니다. position bias·verbosity bias를 알고 보정하는 운영법.

The Illusion of Precision When a benchmark report declares that Model A scores 87.3% on...

Traditional RAG evaluation relies on human-annotated "standard answers," but in the GraphRAG era,...

A complete hands-on guide to building Retrieval Augmented Generation on AWS Bedrock with pgvector,...