A decade of content and no way to review it all at human pace.
I designed a four-stage workflow where AI handles extraction and flagging and the expert focuses on judgment calls. The system supports the review. It does not replace the reviewer.
Human review is essential, but it doesn't scale.
A large content catalog spanning two platforms. The quality audit found that many products, some over a decade old, no longer met current requirements. Expert review was essential. There was not enough expert time to do it all.
Human Decisions.
The Content Specialist sets the criteria. The AI briefing makes the review faster and more targeted. Then the Content Specialist evaluates the output.
A four-stage review where judgment stays human.
The model generalizes across content domains. Each specialist adapts the evaluation framework to their area.
- Map content to requirements at page level
- Compile terminology with source locations
- Build reference inventory across all material
- Coverage confidence: substantive treatment, not surface mention
- Gap and bridge opportunities for new material
- Quality flags: outdated content, sensitivity concerns
- Accessibility: language complexity, vocabulary load
- Evaluate whether AI assessments hold up
- Triage flagged issues: real problems or false positives
- Catch what the AI missed
- Final coverage map: met, partial, or missing
- Prioritized remediation recommendations
- Documentation that accelerates the next cycle
Drawing the line.
The workflow redraws where human attention goes.
Directed attention.
An expert without a briefing has to read everything to get a sense of the gaps. With the AI providing an initial review, the expert knows where to review.
Every page looks the same. The expert reads all of it with equal attention, hunting for where problems might be.
The briefing marks what needs scrutiny. The expert's time goes to the key judgment calls.
Expert attention is finite. Knowing where to direct it changes everything.