01 / THE APPROACHBACK TO INDEX
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05 / 06 — MEASURE WHAT ACTUALLY HAPPENED

Putting it
to the test.

A completed generation is not the same thing as a good message.

The test set

Fifty constructed cases use a near-balanced scenario split: nine greetings, nine recommendations and eight each for care, reactivation, invitation and price objection. They include different client references, uses, price tiers and contact boundaries. All are fictional; this is not a representative sample of real clients.

Three different kinds of evidence

Engineering tests check program behaviour with isolated protocol doubles. The real-model batch evaluates message generation with a fixed scenario. Real-model intent-classification accuracy would require a separate labelled evaluation. One metric must not be substituted for another.

The rubric

Each draft is rated for brand fit, personalisation, naturalness, groundedness, non-intrusiveness, diversity and safety, from one to five. Good requires every dimension at four or above. Acceptable means all are at least three but some need improvement. Any hard failure, or a score at two or below, is not acceptable.

The actual selected batch

The selected submission batch has fifty completed cases and 150 drafts, with 150 AI reviews. It retains 49 v7.5 generations and one v7.6 targeted correction for T43. Each original version and run ID is preserved. All saved sets also passed a v7.6 offline rule replay; that replay is not a new generation or a human review.

The limits of the judge

Generation and evaluation use the same model in separate calls, so they can share blind spots. Human review now covers 150/150 saved drafts. Scores and notes were entered by the submitter, validated and imported separately. This is one reviewer's assessment, not a blinded or multi-rater study.

Seven review dimensions

  1. Brand fit
  2. Personalisation
  3. Naturalness
  4. Groundedness
  5. Non-intrusiveness
  6. Diversity
  7. Safety

The recorded result

Batch: v7.6-submission-20261107-01. Model: AWS-Claude-Opus-5. These are fixed-batch observations, not live performance statistics.

SELECTED BATCH / FIXED SCENARIOS
150DRAFTS
REVIEWED BY AI
GOOD50
NEEDS EDITING61
AI-FLAGGED39

150 HUMAN RATINGS IMPORTED. AI VERDICTS ARE NOT HUMAN CONCLUSIONS.

50 cases completed; 150 drafts. AI verdicts: 50 good, 61 acceptable and 39 not acceptable. Human ratings imported: 150.

The human review

150/150 drafts reviewed by the submitter. Original scores, notes and generated messages are retained.

VerdictHumanAI
Good5450
Needs editing6661
Not acceptable3039

Verdicts agree on 112/150 drafts (74.7%); 38 differ. These are two assessments of the same saved outputs, not a new model-quality improvement.