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  "title": "Evaluations: How We Know an AI Workflow Improved",
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  "thesis": "Evaluations turn vague quality claims into testable checks by defining what 'good' means, collecting evidence, and distinguishing real improvement from noise or gaming.",
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  "publishedAt": "2026-06-29",
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    "title": "AI, De-Mystified",
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      "claim": "An evaluation is a test that turns a quality claim into a repeatable, observable result.",
      "confidence": "high",
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      "evidence": [
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          "snippet": "The survey frames LLM evaluation as a cornerstone of responsible development, organizing it around knowledge and capability evaluation, alignment evaluation, and safety evaluation.",
          "supports": "background",
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          "summary": "Some dimensions of quality, such as creativity or conversational rapport, are hard to operationalize into repeatable tests and may rely partly on subjective judgment.",
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      "claim": "Benchmarks, report cards, and clinical trials all evaluate outcomes against a standard; AI evaluation extends the same idea to generated outputs and workflows.",
      "confidence": "high",
      "status": "landscape",
      "evidence": [
        {
          "sourceId": "source-helm",
          "snippet": "HELM taxonomizes scenarios and metrics for language models and evaluates models under standardized conditions, treating evaluation as a transparent, shared measurement exercise.",
          "supports": "background",
          "assessedAt": "2026-06-29"
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      "counterevidence": [
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          "summary": "AI outputs are often open-ended and context-dependent, so defining the 'standard' is more contested than in report cards or clinical trials with fixed rubrics and endpoints.",
          "assessedAt": "2026-06-29"
        }
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      "claim": "A practical AI evaluation usually mixes automatic checks, human judgments, and task-specific metrics rather than relying on a single score.",
      "confidence": "medium-high",
      "status": "design",
      "evidence": [
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          "sourceId": "source-helm",
          "snippet": "HELM adopts a multi-metric approach, measuring accuracy, calibration, robustness, fairness, bias, toxicity, and efficiency across scenarios.",
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          "summary": "For narrowly defined tasks, such as passing a fixed set of code tests, a single automatic metric can be sufficient and easier to reproduce.",
          "assessedAt": "2026-06-29"
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      ]
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    {
      "id": "claim-004",
      "claim": "A high score on a benchmark can hide failure modes that matter in real use, because no metric captures every kind of usefulness or harm.",
      "confidence": "medium",
      "status": "risk",
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          "sourceId": "source-big-bench",
          "snippet": "BIG-bench finds that tasks with brittle metrics or multi-step reasoning can show poor absolute performance even when models improve with scale, highlighting limits of narrow benchmarks.",
          "supports": "indirect",
          "assessedAt": "2026-06-29"
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          "sourceId": "source-mmlu",
          "snippet": "Models can score above random chance on MMLU while still showing lopsided performance and near-random accuracy on socially important subjects such as morality and law.",
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          "assessedAt": "2026-06-29"
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      "counterevidence": [
        {
          "summary": "Some benchmarks are designed to correlate with downstream tasks, and strong performance on them can genuinely predict useful behavior in related settings.",
          "assessedAt": "2026-06-29"
        }
      ]
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      "title": "Holistic Evaluation of Language Models (HELM)",
      "url": "https://arxiv.org/abs/2211.09110",
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      "title": "Beyond the Imitation Game Benchmark (BIG-bench)",
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