Building an editorial QA for AI-citable pages: pre-publish checklist

An editorial QA for AI-citable pages verifies claim grounding, heading hierarchy, answer-first structure, schema validity, link health, and metadata consistency before publish. It reduces the risk of LLMs misciting or skipping the page.

TL;DR: Before publishing any page you want LLMs to cite, run a 6-step QA covering factual grounding, structure and hierarchy, answer-first formatting, structured data, link health, and metadata consistency. Each step has a hard pass/fail criterion, and a two-pass review (author + editor) on a typical 1,500-word article usually fits inside 25-40 minutes.

Why a pre-publish QA matters

Generative engines like Google AI Overviews, Perplexity, ChatGPT Search, Claude, and Gemini do not cite every page they read. They prefer pages that are easy to extract, easy to verify, and consistent across signals. A page can rank well in classic SEO and still be skipped by an answer engine because a single weak section confuses the extractor or because the on-page FAQ disagrees with the structured data.

A pre-publish editorial QA closes that gap by catching the most common citation-blockers before the URL goes live, so you do not have to rework after measuring. This checklist is intended for content strategists, editors, and SEO specialists working on pages that target AI search and answer engine traffic. It assumes the draft already exists and is otherwise ready for publish.

The 6-step pre-publish checklist

Run the steps in order. Each step has a hard gate — if a check fails, fix it before continuing.

1. Factual grounding

Strong claims (numbers, absolute statements, platform-specific behaviors) must be verifiable.

[ ] Every numeric claim cites a primary source: a vendor doc, a peer-reviewed paper, or first-party data.
[ ] Platform-specific statements ("Perplexity does X", "ChatGPT cites Y") are dated and link to the official documentation page.
[ ] Anything that cannot be verified is rewritten as a generic statement or removed.
[ ] No invented case studies, no fabricated quotes, no synthetic statistics.

If a claim is important but unverifiable, soften the language ("typically", "in most cases") and remove the specific number. LLMs deprioritize sources that contradict their other signals; one weak claim can cost the citation for the whole page.

2. Structure and hierarchy

Clean structure is one of the strongest predictors of extractability.

[ ] Exactly one H1, matching the title in frontmatter.
[ ] Headings descend without skipping levels (H1 → H2 → H3, never H1 → H3).
[ ] Each H2 introduces a self-contained idea readable out of context.
[ ] No heading is longer than 70 characters.
[ ] Paragraphs stay under four sentences; lists are used for enumerable content.

Run a quick outline scan: if a human reader can grasp the gist of the page from headings alone, an LLM extractor can too. If your headings are creative ad copy rather than descriptive, rewrite them to describe what the section answers.

3. Answer-first formatting

Answer engines reward pages that surface the answer in the first 80 words and again in a short summary. The first one or two sentences of the page often get extracted as the citation snippet, so they must be clean factual statements — not a hook, not commentary.

[ ] An AI summary blockquote sits directly after the H1.
[ ] A TL;DR follows in 2-3 sentences and stays snippet-eligible (under 320 characters).
[ ] The first paragraph after the TL;DR defines the topic in plain language and passes the "opening-sentence extraction test".
[ ] At least one FAQ block at the end of the page uses ### Q: headings with 2-4 sentence answers.
[ ] Each definition or how-to step can stand alone without surrounding context.

4. Structured data and schema

Machine-readable signals confirm to the engine that the page is what it claims to be. Google's own docs recommend two validators: the Rich Results Test for Google-specific eligibility, and the Schema Markup Validator for generic schema.org validation.

[ ] A JSON-LD block is present: Article, FAQPage, or HowTo depending on content type.
[ ] headline, author, datePublished, dateModified, and description are populated.
[ ] FAQPage schema mirrors the on-page FAQ exactly — no drift in question or answer text. (FAQ drift is a frequent cause of "unnamed item" warnings in the Rich Results Test.)
[ ] Schema validates against both validators with no errors. Warnings are acceptable but documented.
[ ] OpenGraph and Twitter card metadata match the on-page title and description.

5. Link health and internal linking

Citations follow trust graphs. Broken or thin links lower the page's authority signal.

[ ] Every external link returns 200 and points to a still-relevant page (not a redirect chain or archived tombstone).
[ ] At least one internal link points to the section hub or pillar page.
[ ] 2-3 internal links point to sibling articles listed in related_articles.
[ ] No orphan: this URL is linked from at least one indexed hub page.
[ ] Anchor text is descriptive — not "click here" or "this article".

6. Metadata consistency

The frontmatter, the visible page, and the structured data must tell the same story.

[ ] title in frontmatter matches the H1 and the og:title.
[ ] description (120-160 characters) matches the meta description and the og:description.
[ ] canonical_url matches the rendered URL.
[ ] slug and section match the file path.
[ ] published_at and updated_at match datePublished and dateModified in JSON-LD.
[ ] canonical_concept_id is unique and kebab-case; no other page in the corpus uses the same value.

How to run the checklist efficiently

Two passes are usually enough.

Pass 1 — author self-review. The author works through steps 1-3 (grounding, structure, answer-first) before handing off. This is where most defects originate, and the author has the strongest context to fix them.

Pass 2 — editor review. A second editor handles steps 4-6 (schema, links, metadata) using a validator and a link checker. Fresh eyes catch consistency drift that the author cannot see, especially after a long edit cycle.

For a 1,500-word article both passes together typically run in 25-40 minutes. Pages that consistently pass in under 20 minutes usually have a strong template; pages that take over an hour usually need a structural rewrite, not more polish.

Common mistakes to watch for

Updating the on-page FAQ but forgetting to update the matching FAQPage schema.
Letting description drift between frontmatter, meta, and OpenGraph during edits.
Adding a new section without re-running the heading hierarchy check.
Citing a vendor blog post instead of the vendor's documentation when both are available.
Leaving placeholder dates in JSON-LD after a content refresh.
Opening the article with a creative hook instead of a clean factual sentence — LLMs will skip it and cite a competitor whose opener is extractable.

When to reject a page from publish

Reject (do not publish) if any of these fail:

A core claim is unverifiable and load-bearing for the page's argument.
The page has no AI summary block, no TL;DR, or no FAQ.
Schema validation returns errors (warnings are acceptable).
The page contradicts an existing canonical page on the same canonical_concept_id.

In all other cases, send the page back for revision rather than rejecting outright. A reject should be rare; the QA exists to make revision cheap.

FAQ

Q: How long should a pre-publish QA take?

For a 1,500-word article, a two-pass QA usually takes 25-40 minutes total. Pages that take significantly longer often have structural issues that polishing will not fix; rewrite them instead of running the QA repeatedly.

Q: Do I need every step for every page?

For pages targeting AI search and answer engine traffic, yes. For pages that exist only to serve internal navigation (hub pages, redirects, legal disclaimers), steps 4-6 are still required but step 3 (answer-first) can be relaxed.

Q: Can I automate this checklist?

Steps 4 (schema validity) and 5 (link health) are easily automated with the Rich Results Test API, the Schema Markup Validator, and a link checker in CI. Steps 1-3 and 6 still benefit from a human pass because they require judgment about claim strength and stylistic consistency.

Q: What if my CMS does not support custom frontmatter?

Move the equivalent fields into the page template's metadata layer (front-end head tags plus JSON-LD). The QA checks the same signals regardless of how they are declared, so the page can still be citation-ready without a Markdown-style frontmatter.

Q: How often should I re-run the QA on existing pages?

Re-run the full checklist when the page is materially edited, when its canonical concept is updated, or every review_cycle_days interval (default 90 days). Steps 4 and 5 should also be re-run any time you migrate domains or change the URL scheme, since both can break structured data and outbound links silently.