Free Lesson
AI Output That Won't Lie to You
60 min
Jan 11, 2027 9:00 PM
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What you'll learn
See why "it looks good" isn't a measurement
Name the false-negative that ships — the confidently-wrong sample nobody read — and why eyeballing three fails.
Make "good" a number that clears a gate
Score output against a rubric with named dimensions and a numeric pass threshold, run against real output.
Log every score so it stays defensible
Record scores in an append-only log so last week's pass is auditable this week, not just remembered.
Why this topic matters
Most teams ship AI output on vibes: someone reads three samples, says "reads well," and it goes live. Then the fourth — the one nobody read — is confidently wrong, and a user finds it first. "Looks good" was never a measurement. The move this hour proves: make "good" a number that clears a gate. I show a real, shipped rubric running live on actual output, and the append-only log keeping every score auditable. Detect stage.
