--- title: "LLM Evals Are Based on Vibes — I Built the Missing Layer That Decides What Ships" url: https://daily.dev/posts/llm-evals-are-based-on-vibes-i-built-the-missing-layer-that-decides-what-ships-epggymxbj source_url: https://towardsdatascience.com/llm-evals-are-based-on-vibes-i-built-the-missing-layer-that-decides-what-ships type: article source: "Towards Data Science" published: 2026-05-17T13:13:56.824Z updated: 2026-05-17T13:14:25.185Z tags: ["python", "llm", "rag"] reading_time: 25 upvotes: 0 comments: 0 language: en --- > ## Documentation Index > Fetch the complete documentation index at: https://daily.dev/llms.txt > Use this file to discover all available pages before exploring further. # LLM Evals Are Based on Vibes — I Built the Missing Layer That Decides What Ships **[Towards Data Science](https://daily.dev/sources/tds)** · 25 min read · 0 upvotes · 0 comments ## Summary Most LLM evaluation systems rely on a single composite score and human judgment, which fails to catch confident hallucinations that pass threshold checks. This post presents a lightweight Python evaluation layer that splits faithfulness into two separate signals — attribution (grounding in context) and specificity (concreteness of claims). High specificity combined with low attribution is the hallmark of a confident hallucination. The system adds a decision layer that converts scores into actionable verdicts (ACCEPT, REVIEW, REJECT) with plain-English reasons and next actions. It runs locally using sentence-transformers in ~291ms with no API calls by default, escalating to an LLM judge only for borderline scores (0.45–0.65). A disagreement signal (standard deviation across dimension scores) routes high-variance outputs to human review. A regression suite integrates with CI/CD to block deployments when prompt changes degrade quality. Limitations include inability to detect implicit semantic drift or cross-document contradictions. ## Full article daily.dev links to this article rather than hosting it. Read it at the original source: ## Similar posts on daily.dev - [Stop Evaluating LLMs with “Vibe Checks”](https://daily.dev/posts/stop-evaluating-llms-with-vibe-checks--aiyxjrdvh) · Towards Data Science · 1 upvotes · 1 comments - [How to evaluate LLMs before production](https://daily.dev/posts/how-to-evaluate-llms-before-production-ps4g4zhbm) · GitHub Blog · 1 upvotes · 0 comments - [LLM evaluation metrics explained: how to measure AI system quality](https://daily.dev/posts/llm-evaluation-metrics-explained-how-to-measure-ai-system-quality-ynrvblcpe) · Netguru · 0 upvotes · 0 comments - [Measuring an LLM’s Confidence Instead of Trusting It](https://daily.dev/posts/measuring-an-llm-s-confidence-instead-of-trusting-it-twyzb7eja) · Medium · 1 upvotes · 0 comments - [When the Model Is Confident and Wrong: A Practitioner Guide to LLM Output Reliability](https://daily.dev/posts/when-the-model-is-confident-and-wrong-a-practitioner-guide-to-llm-output-reliability-thcxhmnkt) · SD Times · 0 upvotes · 0 comments --- Tags: [#python](https://daily.dev/tags/python), [#llm](https://daily.dev/tags/llm), [#rag](https://daily.dev/tags/rag) [View this post on daily.dev](https://daily.dev/posts/llm-evals-are-based-on-vibes-i-built-the-missing-layer-that-decides-what-ships-epggymxbj)