{
  "schemaVersion": 1,
  "lessonId": "operations-07",
  "contentHash": "43e1f34e84f93a58fe344a5357aee212ede59116f582807c39c167b4ae0f84a7",
  "executedAt": "2026-10-20T10:51:53.818799+00:00",
  "environment": {
    "backend": "cpu",
    "deviceCount": 1,
    "python": "3.14.3",
    "jax": "0.9.2",
    "numpy": "2.4.4",
    "matplotlib": "3.10.8"
  },
  "stdout": "Observed replay scores: [{'overall': 0.9, 'answerable': 1.0, 'unanswerable': 0.0}, {'overall': 1.0, 'answerable': 1.0, 'unanswerable': 1.0}]\nTwo real MLflow traces; no LLM API was called and no general model quality was evaluated.\nObserved replay scores: [{'overall': 0.9, 'answerable': 1.0, 'unanswerable': 0.0}, {'overall': 1.0, 'answerable': 1.0, 'unanswerable': 1.0}]\nTwo real MLflow traces; no LLM API was called and no general model quality was evaluated.\nRelease decisions: [False, True]\nComplete inventory: {'count': 10, 'passed': 9, 'fraction': 0.9} ; missing, duplicate and unknown responses rejected.\nUnrecognized citations rejected.\nAn abstention flag alone does not satisfy the output contract.\nFailed answerable work remains in denominator: {'count': 10, 'passed': 9, 'fraction': 0.9}\nPASS: operations-07\n",
  "notebookCells": [
    {
      "sourceHash": "649769c1c54e070b7d4767e14a65cfabfe3712cf5a99be19fe8abc01e2a7d03f",
      "outputs": []
    },
    {
      "sourceHash": "0dfba4ba6cc1982b4d817b478bb8dee7b41286555b71eb684837ba10d8915657",
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Observed replay scores: [{'overall': 0.9, 'answerable': 1.0, 'unanswerable': 0.0}, {'overall': 1.0, 'answerable': 1.0, 'unanswerable': 1.0}]\n",
            "Two real MLflow traces; no LLM API was called and no general model quality was evaluated.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "2026/10/20 03:51:52 INFO mlflow.store.db.utils: Creating initial MLflow database tables...\n",
            "2026/10/20 03:51:52 INFO mlflow.store.db.utils: Updating database tables\n",
            "/Users/tahabsn/Documents/ChatGPT/jaxpathways/phases/16-operations/07-llmops-prompts-evaluation-and-traces/notebooks/exercise.ipynb:cell-2:35: FutureWarning: Parameter 'experiment_ids' is deprecated. Please use 'locations' instead.\n"
          ]
        }
      ]
    },
    {
      "sourceHash": "ef19eb67ec0ddfa276e9d473b7b64bd41898dde83991f88c633d7c9a0c94854f",
      "outputs": []
    },
    {
      "sourceHash": "c905358810448a1648ef26cc14654084b28bd7fed27d09a01dfe55335ff22500",
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Observed replay scores: [{'overall': 0.9, 'answerable': 1.0, 'unanswerable': 0.0}, {'overall': 1.0, 'answerable': 1.0, 'unanswerable': 1.0}]\n",
            "Two real MLflow traces; no LLM API was called and no general model quality was evaluated.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "2026/10/20 03:51:52 INFO mlflow.store.db.utils: Creating initial MLflow database tables...\n",
            "2026/10/20 03:51:52 INFO mlflow.store.db.utils: Updating database tables\n",
            "/Users/tahabsn/Documents/ChatGPT/jaxpathways/phases/16-operations/07-llmops-prompts-evaluation-and-traces/notebooks/exercise.ipynb:cell-4:49: FutureWarning: Parameter 'experiment_ids' is deprecated. Please use 'locations' instead.\n"
          ]
        }
      ]
    },
    {
      "sourceHash": "917c08bd4f5fbf6ac3e258e355733e76d9d33405e06fcdee53f40787843b11bc",
      "outputs": []
    },
    {
      "sourceHash": "25df6d662f9d3f0c4e984c6a201f79fa0cfd7a2633dc4b0773dfba6c2dd4ee6c",
      "outputs": [
        {
          "output_type": "display_data",
          "metadata": {},
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",
            "text/plain": [
              "Executed Matplotlib figure"
            ]
          }
        }
      ]
    },
    {
      "sourceHash": "d0da9513a894f50512d7fb5c2e33d823df4e7b5a72ea105a56451b1d8bcf41aa",
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Release decisions: [False, True]\n"
          ]
        }
      ]
    },
    {
      "sourceHash": "704122e566e69d78148a71e3d119bc99631db134b03bcce674ff5c84095d6a28",
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Complete inventory: {'count': 10, 'passed': 9, 'fraction': 0.9} ; missing, duplicate and unknown responses rejected.\n"
          ]
        }
      ]
    },
    {
      "sourceHash": "64278ad5035044aa440acd6ca50dd5448e94eda116a59e4ac768a055145e96eb",
      "outputs": []
    },
    {
      "sourceHash": "f5675239ac33959a79302cd33ee4a4496e77d50294fc338ea32d836985663a4c",
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Unrecognized citations rejected.\n"
          ]
        }
      ]
    },
    {
      "sourceHash": "9628b09d1c566958581cd44ed3ddf625894e70d235bfa76fd80de6b7cec40180",
      "outputs": []
    },
    {
      "sourceHash": "bb339057af1f941eee45020cca5d32461f801b1ab77a88e0e28a06cea974dff0",
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "An abstention flag alone does not satisfy the output contract.\n"
          ]
        }
      ]
    },
    {
      "sourceHash": "fc6ead310be8994aae1e961a8c56a6c283317043f89f77b5e26bf3a144a8057a",
      "outputs": []
    },
    {
      "sourceHash": "e4cf538ec29536d02017fe041fa1e8acdc886e2dd68cba6a1936c5ea5f2b0ebb",
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Failed answerable work remains in denominator: {'count': 10, 'passed': 9, 'fraction': 0.9}\n"
          ]
        }
      ]
    }
  ],
  "scope": "Recorded instructor reference run; all script assertions and notebook code cells passed. No accelerator performance claim."
}