What should a GEO content checker actually measure?
Classic content graders optimize for keyword density and readability scores — metrics aimed at ranking algorithms. AI engines need something different: passages they can lift, facts they can verify, entities they can reconcile. A GEO checker therefore scores five dimensions, each mapped to a stage of the retrieval-grounding-generation-citation pipeline described in how LLMs choose sources:
- Answer-block presence. Does the page open its key sections with a direct, self-contained answer — 40 to 80 words that could stand alone if extracted? The pattern is defined in answer block writing.
- Extractability. Are passage boundaries clean — question-form headings, single-idea paragraphs, lists and tables where appropriate? Could a retrieval system lift one passage without dragging in unrelated text?
- Fact density with provenance. How many specific, dated, sourced claims per page — numbers with a study and a date, named entities, verifiable statements? Undated, unsourced superlatives score zero.
- Consistency risk. Do the page’s claims match the rest of your source graph — other pages, profiles, marketplace listings? Flag contradictions before grounding finds them; see grounding fact consistency.
- Structural hygiene. Valid structured data, crawlable HTML, fast fetch — the prerequisites checked by schema tools and log analysis.
How do you score pages in practice?
Start manual; automate only when the rubric is stable. A workable rubric scores each dimension 0-20, producing a 0-100 citability score per page, with hard gates: no answer block on the primary question caps the score at 50; any factual contradiction with the source graph caps it at 40. Run the rubric on your top pages quarterly and on new content before publication. The output is not a vanity score — it is a prioritized fix list: this page needs an answer block, that page needs dated sources, this one contradicts your own pricing page.
What should you demand from automated checkers?
- Transparent criteria — you should be able to see why a score was given and reproduce it by hand.
- Provenance detection — does it notice that a claim lacks a date or source, or does it just count keywords?
- Consistency checking across your own corpus, not within a single page only.
- Honest scope — a checker predicts extractability; it cannot guarantee citations. Any tool promising guaranteed AI rankings is selling fiction.
How does checker output feed the measurement loop?
Page-level citability scores are the leading indicator; engine-level citation data is the lagging one. The loop: score pages, fix the top deficits, then watch whether the fixed pages begin appearing as sources in your monthly prompt runs — the instrument described in AI answer simulators. When fixes do not translate into citations after a full cycle, investigate access (crawler logs) before rewriting content again.
How often should pages be re-scored?
Quarterly for the stable library, immediately for anything rewritten, and after any engine change that alters what gets cited. Scores age: a page that scored well against last quarter’s citation patterns may have new gaps after a model upgrade shifts passage preferences. Keep the rubric itself under version control so score changes mean content changes, not rubric drift.
Common pitfalls
Scoring everything and fixing nothing is the classic failure — the rubric exists to prioritize, not to report. Optimizing pages nobody asks about wastes the effort; start from your fixed question set. Chasing a perfect score on one page while ten buyer-critical pages have no answer blocks misallocates effort. And treating the checker as a substitute for measurement inverts causality: scores are hypotheses, citations are evidence.
Where to go next
Learn the writing pattern at answer block writing, see citation-worthy standards at citation-worthy content, and run a diagnostic at the GEO Checker.