AI SEO Projects ·Content & Answer Optimization
Spec-table restructure
Last reviewed:
- Owner
- Editorial + design system
- Metric
- Spec-answer accuracy from assistants, before and after, per flagship product
- Clock
- Retrieval layer —the two clocks
Reliability drops sharply on complex tables — precisely the format of hardware spec sheets and edition-comparison grids. This is the content-side companion to the infrastructure extractability scorecard: infrastructure sets the semantic structure standard and the design-system component; this project authors the flagship spec content to it and proves the gain.
Why this, mechanically. A model asked “how much battery does this laptop have” fails on an irregular spec grid and succeeds on clean, headed, semantic HTML with a prose summary. Rebuilding the spec presentation converts the catalog’s most valuable and least parseable content into content a model can quote — without surrendering the premium visual design, because the design-system component solves that trade-off once.
Deliverable. The priority product spec tables rebuilt into semantic HTML with prose summaries, using the spec-block component, with before/after extraction accuracy measured by spec prompts to the major assistants.
Finish line. Flagship spec pages are migrated, and assistant spec answers are measurably more accurate than before the rebuild.
Verify structure with the content checklist; the audit procedure is audit content extraction.