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Tiện Ích Gia Đình
Tiện Ích Gia Đình
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Saturday 15 November 2025 11:05:00 GMT
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"Does it feel better?" is not an eval. It's a vibe. And it's why your AI app regresses in prod without anyone noticing. Wrong question: "is the new prompt better?" Real question: "better on which dimension, and where exactly did it fail?" Five stages. One closed loop. 🧪 RUN — the same suite every release. 6 cases through the app. 4 green, 2 red. The reds have names: wrong tool, unsupported claim, bad format, slow. ⚖️ GRADE — three judges, three jobs. Code grader for exact output. Model grader for open-ended quality. Human review for the ambiguous edge cases. One grader cannot see all three. 📊 SCORE — six dimensions, never one number. Correctness 0.82 · Groundedness 0.71 · Tool success 0.88 · Safety 0.99 · Latency p95 1.9s · Cost $0.004 Only groundedness is red. A single "accuracy: 87%" would have buried it. 🔍 DIAGNOSE — bucket the failures. Retrieval 11 · Prompt 5 · Tool 3 · Policy 2 · Format 1 Retrieval towers over everything. That is why groundedness is low — the model was never given the right context to ground on. 🔁 FIX & RERUN — patch retrieval, rerun only the failed cases. Groundedness 0.71 → 0.91. All 6 green. Release gate flips BLOCKED → READY. 👀 The part teams skip: the buckets. Scores tell you something is wrong. Buckets tell you what to touch. Without them you rewrite the prompt for a week to fix what was a retrieval bug the whole time. The rule: a score is a symptom, a bucket is a diagnosis. Most teams ship on a demo that felt good, then find out from users. Send this to whoever just said "yeah it seems smarter now." 📸 Screenshot the last frame — the whole loop, scored and gated, on one card. Follow @hackproduct — scary AI concepts, made shippable. ⚡ . . #AIevals #LLMevaluation #AIengineering #LLM #RAG

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