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AI Foundations

Scoring & Quality Framework

Every AI output is graded against versioned rubrics, so quality is measured, not assumed.

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TRIGGER Output produced article, post, mail, reply DATA STEP Load rubric versioned criteria AI STEP Score each criterion with evidence DATA STEP Total in code no model arithmetic HUMAN CHECKPOINT Calibrate compare with human verdicts RESULT Pass / fail gate block or release TRIGGER Output produced article, post, mail, reply DATA STEP Load rubric versioned criteria AI STEP Score each criterion with evidence DATA STEP Total in code no model arithmetic HUMAN CHECKPOINT Calibrate compare with human verdicts RESULT Pass / fail gate block or release
TriggerData stepAI stepHuman checkpointResult

Aggregated main logic. Real projects add error handling, retries and logging around every step.

The problem

AI output varies. Without objective checks you find out about weak work from customers.

  1. Each content type has criteria that are separate, clear and tested with good and bad examples.
  2. Scoring happens per criterion, and the total is computed by code, not by the model.
  3. Rubrics are immutable versions: changes ship as a new version and old scores stay valid.
  4. Calibration against human decisions shows whether the scorer can be trusted.

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