LLM Evals Are Based on Vibes — I Built the Missing Layer That Decides What Ships
Many LLM evaluation systems rely on subjective 'vibe checks' that fail at scale, often missing confidently incorrect responses. The author presents a lightweight Python-based evaluation layer that separates faithfulness into attribution and specificity to detect hallucinations. This system acts as a decision engine to determine whether LLM outputs should be shipped, retried, or regenerated based on reproducible metrics.
- ▪Most LLM evaluation methods depend on subjective human judgment, which breaks down when scaling production systems.
- ▪The author's evaluation layer identifies hallucinations by detecting high specificity paired with low attribution, a combination traditional single-score metrics miss.
- ▪A seemingly minor prompt change like 'be specific and detailed' can increase hallucination rates while raising evaluation scores, creating false confidence.
- ▪The system is designed for RAG pipelines and chatbots where automated decisions about response quality are needed before user delivery.
- ▪Fluency and structure in LLM outputs do not guarantee accuracy, and conventional eval methods often fail to catch confidently wrong responses.
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Large Language Model LLM Evals Are Based on Vibes — I Built the Missing Layer That Decides What Ships How I built a lightweight evaluation system that measures faithfulness, detects hallucinations, and turns subjective LLM outputs into reproducible metrics — all in pure Python Emmimal P Alexander May 17, 2026 24 min read Share Image by the author, generated with ChatGPT (DALL·E) TL;DR This article shows a full working implementation in pure Python, with real benchmark numbers. Most teams evaluate LLM responses by reading them and guessing. That breaks the moment you scale. The real problem is not that models hallucinate. It is that nothing catches the confident ones, the responses that score 0.525, pass your threshold, and are quietly wrong.
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Excerpt limited to ~120 words for fair-use compliance. The full article is at Towards Data Science.
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