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

说明与用途

引入多阶段审核流程，将技术有效性与媒体质量区分，帮助识别首要阻碍因素并做出发布决策。

问题

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.

结果

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

工作流

• 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.

证据

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

来源

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

来源

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

来源