Your AI Video Passed QA—and Still Wasn't Publishable

Description and use

A multi-pass review workflow separates technical validity from media quality, enabling identification of first blockers and informed publishability decisions for AI-generated videos.

Problem

AI-generated videos can pass automated QA yet still be unpublishable due to media quality issues such as poor pacing, visual stagnation, and misaligned audio-visual sync.

Outcome

Implemented a structured, multi-pass review workflow that distinguishes technical validity from media publishability, reducing rework and ensuring consistent quality across AI-generated videos.

Workflow

• 1. Run automated QA to ensure technical validity (codec, streams, subtitles, provenance).
• 2. Pass 1: Audio-only review to check pronunciation, pacing, pauses, and clarity.
• 3. Pass 2: Silent visual review to assess background relevance, character movement, framing, and visual continuity.
• 4. Pass 3: Integrated first watch to evaluate audio-visual-captions harmony and overall credibility.
• 5. Record first blocker details (category, segment, evidence) and assign repair scope.
• 6. Decide release verdict (publishable_as_is, publishable_with_minor_edits, revise, redesign) based on pass results.
• 7. Iterate until all passes meet the accepted or stable criteria.

Evidence

Automated QA can prove the first. It cannot prove the other three.

Source

The background did not support the explanation. Sentence boundaries and visual changes disagreed.

Source

I store a review result like this: ... first_blocker: category pacing ...

Source