LLM Evals Are Based on Vibes — I Built the Missing Layer That Decides What Ships
中文摘要
针对LLM评估过于主观的问题，作者开发了Python层，利用归因、具体性和相关性实现客观决策，从而在生产前拦截幻觉。
English Summary
To fix subjective LLM evaluations, the author built a Python layer using attribution, specificity, and relevance to ensure reproducible decisions and catch hallucinations before production.
Original Excerpt
Most LLM evaluation systems rely on vague scoring and human judgment disguised as metrics. I built a lightweight evaluation layer in pure Python that turns LLM outputs into reproducible decisions by separating attribution, specificity, and relevance—so hallucinations are caught before they reach production. The post LLM Evals Are Based on Vibes — I Built the Missing Layer That Decides What Ships appeared first on Towards Data Science.