1.4What AI Vision Models Can Assess

Beyond simple "same or different," AI vision models can provide qualitative assessments:

Assessment	Example	Value
Change classification	"Text color changed from #333 to #666"	Helps reviewers understand what changed
Impact severity	"This change affects the primary CTA button"	Prioritizes review effort
Intentionality guess	"This appears to be a deliberate redesign, not a regression"	Reduces false alarm fatigue
Accessibility impact	"New color combination may fail WCAG contrast requirements"	Cross-discipline insight
Layout analysis	"Navigation items are now overlapping at this width"	Catches functional visual bugs

Prompt Pattern for AI Visual Triage

You are reviewing two screenshots of the same web page: a baseline (known good)
and a current (from the latest build).

Compare the two images and classify the differences:

1. **No meaningful change** -- Anti-aliasing, sub-pixel rendering, font smoothing
   differences. Auto-approve.
2. **Minor change** -- Color shift within 5%, spacing change under 2px, shadow
   difference. Flag as low priority.
3. **Significant change** -- Element position shift, content change, new/removed
   element, color change over 5%. Flag for human review.
4. **Breaking change** -- Element overlap, content overflow, missing content,
   layout collapse. Block the build.

For each difference found, provide:
- Category (1-4)
- Description of the change
- Location on the page (top/middle/bottom, left/center/right)
- Recommendation (auto-approve / review / block)