Independent AI content publishing guides

AI Content Quality Control

A pre-publication QA system for AI-assisted articles.

Quality control should be explicit enough that an editor can reject a draft for a concrete reason rather than relying on vague impressions.

What matters most

  1. Intent match
  2. Accuracy and source quality
  3. Original value
  4. Structure and repetition
  5. Commercial claim integrity
  6. Internal links
  7. Metadata and schema
  8. Mobile readability
  9. Final proofread

Keep the workflow accountable

The useful metric is time to an accurate, internally linked, publishable page—not raw words generated per minute. Search intent, factual verification, source quality, and final editorial judgment remain publisher responsibilities.

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