AI content optimization template for extractability audits
Check whether answer blocks are self-contained, structurally clean, and machine-readable — the properties AI answer engines need to lift a claim.
- Sanity / CMS
- Google Docs
- Notion
- ChatGPT
- Perplexity
Audit extractability on our "ai marketing agent" and "seo automation" pages.
- Read the pagesRendered HTML for 12 commercial and definitional pages
- Scored against rubric6 of 12 failed the self-contained claim check
- Flagged rewrite candidates4 pages marked rewrite; 8 marked tweak
- Produced fix listGrouped by fix type so one pass addresses all 12
Six pages failed the self-contained claim check — their answer sentences referred to "the table above" or "as mentioned." Four are full rewrites; the rest are heading and schema tweaks that ship in one pass.
An ai content optimization template audits the gap between a page that ranks and a page that gets quoted. It checks whether answer blocks are self-contained, whether claims survive being lifted out of context, and whether the structure makes the answer the easiest thing to extract. Ranking is no longer enough — the page has to be extractable.
What is an ai content optimization template?
A per-page audit that scores extractability: whether headings are question-shaped, whether the first sentence answers, whether claims are self-contained, and whether schema marks up the answer. The output is a list of fixes, not a score.
Audit per page
- Score question-shaped headings and answer-first paragraphs
- Flag claims that depend on surrounding context
- Check schema and AI crawler access
Prioritize fixes
- Rank pages by citation potential, not by traffic
- Group fixes by type so one pass fixes many pages
- Mark the pages where a rewrite beats a tweak
Why do ai answer blocks need to be self-contained?
Because an answer engine lifts the sentence, not the paragraph around it. A claim that refers to "the table above" or "as we discussed" fails extraction. Self-contained means the sentence reads true with no surrounding context.
How do question-shaped headings help llm readability?
An answer engine matches a prompt to a heading. "How much does X cost?" is a better heading match than "Pricing." The question shape also helps the human reader who scans, which is why it lifts engagement alongside extractability.
What does an ai content optimization audit check?
Question-shaped headings, answer-first paragraphs, self-contained claims, current dates, machine-readable schema, and AI crawler access in robots.txt. Each is scored per page so the report points at specific fixes.
How the ai content optimization template fits your stack
The agent reads each page from your CMS or the rendered HTML, scores extractability against the rubric, and writes the fix list to Docs or Notion. It pairs with the FAQ extraction rewrite and schema-for-ai-search templates so a single audit produces the worklist for both.
- Sanity / CMS
- Google Docs
- Notion
- ChatGPT
- Perplexity
Who uses this ai content optimization template
- SEO teams
- Move from rankings to citations as the success metric.
- Content leads
- Get a fix list per page instead of generic advice.
- Founders
- See which pages AI answer engines can actually quote.
How to run this ai content optimization template in Metaflow
Point at the page set
The pages you want cited — usually commercial and definitional.
Run the extractability audit
The agent scores each page against the rubric.
Review the fix list
Grouped by fix type so one pass addresses many pages.
Hand off to the rewrite
Pages marked rewrite feed the FAQ extraction rewrite template.
What you provide
- Page set to audit
- Tracked competitors
- Priority prompts
What you get back
- Extractability score per page
- Fix list grouped by type
- Rewrite candidates
- Schema gaps
Why use this ai content optimization template?
Scores extractability, not rankings — the property AI engines need
Flags context-dependent claims that fail lifting
Pairs with the FAQ and schema templates for the fix workflow
Output is a fix list per page, not a generic score
AI content optimization template FAQs
What makes content extractable for AI?
Question-shaped headings, answer-first paragraphs, self-contained claims, current dates, and machine-readable schema. Extractability is the property an answer engine needs to lift a clean claim, which is different from what ranking needs.
How is AEO different from SEO?
SEO optimizes for a ranked list of links; AEO optimizes for being the source an engine quotes inside a synthesized answer. They share fundamentals, but AEO adds extractability — structure that makes a specific sentence liftable.
How do you audit AI extractability?
Read the page as an answer engine would: does the first sentence under a question heading answer the question? Is the claim self-contained? Is the schema present? Score each, then prioritize fixes by citation potential.
Which pages should you audit first?
Definitional pages ("what is X"), comparison pages, and the commercial pages that map to buyer prompts. Those are the ones an answer engine is most likely to quote — or to quote a competitor on.
Similar AEO checklists and AI visibility templates
Key takeaways
- Extractability is the property AI engines need, not what rankings need
- Self-contained claims survive being lifted out of context
- Question-shaped headings help the engine and the human scanner