How to Restructure a Page for AI Extraction During a Refresh

You have a page that says the right things. It still gets skipped when an AI engine answers the exact question it was built for. Most of the time the facts are fine. The answers are just buried under a slow intro, vague headings, and paragraphs doing five jobs at once.

This guide is for the person doing the work: you have already decided this page is worth saving, and now you need an AI extraction refresh that actually changes its shape. By the end, every important question on the page will have its own labeled, self-contained answer that a reader or an answer engine can find without digging.

One thing to set aside. This is structural work only. You are not updating stats or rewriting claims. If you spot a stale fact, put it on a list and send it to your fact-update pass. Hold the facts still and move the furniture.

Worth saying plainly: structure makes answers easier to find. It does not force a citation. Google says normal SEO practice still applies to AI Overviews and AI Mode, and that no special AI file or AI-specific schema is needed there. Treat what follows as a testable editorial hypothesis, not a switch you flip.

Step 1: Map the questions the page must answer

Start with one plain-language question this page exists to answer. Write it in a single sentence. Then list the follow-up questions a reader would ask right before, during, or after that answer. Those questions are your extraction targets.

For each one, keep a small working row:

• the question in natural language
• the answer the page already contains
• the section where that answer lives today
• the section that should own it after the rework
• examples, sources, or internal links already attached to it
• the structural action: keep, move, split, merge, turn into a list, turn into a table, or give it a question heading

Where do the questions come from? Your current headings, the words customers use, your site search log, the questions sales hears every week, and the prompts you already check in AI tools. Google has said AI Overviews and AI Mode may use query fan-out, expanding the original question into related searches. Good reason to cover the natural follow-ups. Not a reason to bolt on every adjacent topic.

If you already track prompts, this is where a tool earns its keep. DeepSmith's AEO module holds the questions you track, with per-prompt mention and citation rates and the answer history behind each one. Discover Prompts generates a starter set from your product, persona, and buyer-stage context, and the Pages view shows which of your pages get cited and which prompts drive them. Use it as an input to your map, not as a replacement for deciding what this page is for.

How to tell it is done: one sentence states the page's primary answer, every planned section owns one clear question, and every question has a home. A question this page cannot honestly answer belongs somewhere else.

Where people go wrong: starting from a keyword list instead of reader questions. You end up writing around phrases, and the page grows sideways until nobody can say what it is about.

Step 2: Inventory the old page's current answer blocks

Read the page twice. Once for meaning, once as a structural map. Write down the title, the H1, every H2 and H3, the opening paragraphs, lists, tables, callouts, links, images, and any structured data.

For every meaningful block, answer four questions:

1. What question is this block answering?
2. Is the answer actually present, and where does it start?
3. Does the block make sense with its heading alone, or does it lean on text somewhere else?
4. What structural change would make the answer easier to find without touching the underlying fact?

Flag the usual suspects: walls of text, headings called "Overview" or "Benefits," sections holding three questions at once, answers that arrive in the fourth paragraph, pronouns with no visible subject, and caveats stranded three sections from the claim they qualify. Flag anything whose answer only exists inside an image or a chart. Important content needs to live in text.

The output is an old-to-new map. The old side names where the answer sits now and what is wrong with it. The new side names the heading that will own it, the answer that will open it, and the format carrying the support.

How to tell it is done: every substantive block has an owner and an action. Nothing is marked "keep as is" without a reason you could say out loud.

Where people go wrong: opening the doc and polishing sentences. A page can have lovely prose and still hide its answer. Diagnose the architecture first. Do not swing the other way either and chop every paragraph in half. A fragment that loses its subject is not an answer unit.

Common mistake: Rewriting the introduction while the real answer stays buried under vague H2s and mixed-purpose paragraphs. Move the answer and rebuild the section boundaries first. Sentence-level polish comes after the outline passes the skim test.

Step 3: Rebuild the outline around question headings

Turn your map into a heading outline someone could follow without reading a word of the body.

• One H1 for the page's central promise.
• H2s for the major questions or tasks, in the order a reader needs them.
• H3s for genuinely distinct subquestions or substeps, not for decoration.
• Headings specific enough that you can predict the answer underneath.
• Action wording on a procedural page, like "Move each answer to the top of its section" instead of "Structure."

Question headings help because they label the retrieval problem out loud. "What makes a passage easy to extract?" tells you what sits below it. "Key insights" tells you nothing. Use a question when that is how the reader would ask it, and a task heading when the page is procedural. Google's guidance says headings help people navigate long pages, and it prescribes no heading count or pattern. No formula to chase here.

How to tell it is done: read only the H1, H2s, and H3s. The outline should tell a coherent story and expose the page's answers on its own. No heading should force you to read three paragraphs to learn its topic, and no two headings should be fighting over the same answer.

Where people go wrong: stuffing keywords into headings, which creates near-duplicate sections and a noisy outline. The other trap is making every heading a question when a short task heading would be clearer.

Step 4: Move each answer to the top of its section

Under every major heading, use the same shape:

1. State the direct answer in the first sentence or two.
2. Name the subject and the condition so the passage can stand alone.
3. Then give the explanation, the steps, the example, the evidence, the caveat.
4. Keep each exception next to the claim it qualifies, not several sections later.

Your introduction gets the same treatment. Say what the page answers before you set any scene. A CXL study of 100 AI Overview citations found 55 percent of the cited material came from the first 30 percent of the page, 24 percent from the middle, and 21 percent from the bottom 40 percent, and it recommends putting your clearest core answer in the first 150 to 200 words. One small observational study, so treat it as a target worth testing rather than a rule. The same article reports a separate analysis of 1.2 million search results and 18,012 verified ChatGPT citations, where 44.2 percent of citations came from the first 30 percent of a document. Two samples, one direction: front-load the answer.

Here is the before and after in miniature. Before: an intro describes the industry problem, a second paragraph defines four terms, and the recommendation shows up halfway through a section called "Benefits." After: the heading names the question, the first sentence gives the recommendation, the next paragraph explains why, and a short list carries the steps.

Use the pattern in your FAQ too. The CXL study notes FAQ blocks can still pick up bottom-of-page citations when each question is specific, because every question and answer pair behaves like a tiny article with its own answer on top.

Pro tip: Draft the first sentence under every H2 on its own, before you polish anything else. Then read only those sentences in order. If they do not form a useful summary of the page, the structure is not finished yet.

How to tell it is done: someone can skim your intro plus the first sentence under each heading and walk away with the main answer.

Where people go wrong: a generic preamble dressed up as an answer, or an opener that says "it depends" without naming what it depends on. Give the useful default first, then the conditions.

Step 5: Split dense copy into clean extraction chunks

A clean chunk is a local answer unit that still makes sense when someone meets it alongside its heading and nothing else. It usually carries one claim, one task, one definition, one example, or one decision rule. The goal is not to make every paragraph tiny. It is to stop three unrelated answers being welded into one block.

For each chunk:

• keep the subject explicit instead of leaning on "it," "this," or "they"
• keep the condition, exception, and audience qualifier next to the claim
• use a short paragraph to explain, a numbered list for a sequence, bullets for parallel criteria, a table for labeled comparisons
• introduce every list and table with a sentence saying what it shows
• give every table useful column headers
• drop "as noted above" and "the following" when the reference is not obvious
• keep enough connective prose that a reader sees how the units relate

Bing's own AI guidance points at clear headings, tables, and FAQ sections because they surface key information and make content easier for AI systems to reference accurately. Good news: those are formats you already know. It is not a licence to turn every paragraph into bullets.

Then run the copy-out test. For every H2 and H3, copy the heading plus its first paragraph or two into a blank document and ask: can a reader name the subject without the rest of the page? Is there one clear answer? Are the conditions there? Does the opening use the same plain words as the question? If not, add local context, pull the support into the same section, split competing intents, or merge fragments too thin to stand.

This is the step that does the most to make content extractable. Each unit can be lifted out and still be true.

How to tell it is done: every important section passes the copy-out test, and the page can be read as a skim path of headings, opening answers, lists, and tables.

Where people go wrong: over-chunking. A page of disconnected one-line fragments is short and useless. Keep the qualifier attached to the claim, and use a short bridge sentence when the relationship between two chunks actually matters.

Step 6: Attach evidence and links to the right claims

Keep support next to the answer it supports. When the old page already carries sources, examples, or data, preserve the link between claim and evidence while you move things around. The rule is short: answer first, support second, link at the point of need.

Descriptive anchor text does real work here. Google's SEO guidance says links help people and search engines reach relevant pages, and recommends anchor text that says what the linked page is about before they click. Bing's guidance says examples, data, and cited sources build trust when content gets reused in an AI answer. "Read more" gives neither anything.

A few format calls while you are in there. Use a table only when the column labels make the comparison mean something. If the section describes a process, put the steps in order and keep each condition beside its action. If it defines a term, define it before the history.

Finding the right internal targets is the part that quietly eats an hour per page. DeepSmith's Content Map crawls your site and your competitors' onto one topic and funnel-stage taxonomy, refreshes every 24 hours, and powers the internal-linking workflow, so candidate pages come to you instead of you hunting through a sitemap. If the page needs replacing rather than reworking, Content Studio's Writer produces a brand-grounded article with research, internal and external links, metadata, and a cover image already in place. It is not a one-click structural edit of a live URL.

How to tell it is done: every important answer carries the support you need to believe it, every internal link has descriptive anchor text, and no orphaned link list sits at the bottom waiting to be matched to claims by hand.

Where people go wrong: link dumping. Adding links to loosely related pages makes a page look connected without helping anyone find an answer. Fewer and more relevant wins.

Step 7: Make the page readable to crawlers and answer engines

This is a technical and semantic QA pass, not a request to add AI markup. Work the list:

• Important answers exist as indexable text, not only inside an image, a canvas, or a tab that never renders its content.
• The title and H1 are unique, clear, and an honest description of the page.
• The visible headings and body agree with the title and metadata.
• Any structured data describes what is actually visible and validates cleanly. Google uses structured data to understand content, and it does not require special AI schema for AI Overviews or AI Mode.
• No FAQ schema added purely to chase a citation. A visible FAQ can stay because it helps readers, which is a different reason.
• Crawling is allowed by robots.txt and by whatever your CDN or host is doing.
• The page is indexable and eligible for a snippet. Check for an accidental noindex, nosnippet, data-nosnippet, or a max-snippet setting quietly suppressing the answer.
• If ChatGPT Search matters to you, you have not opted out of OAI-SearchBot. OpenAI says that bot surfaces sites in ChatGPT Search, and opted-out sites will not appear there.

Google has said a supporting page must be indexed and eligible to appear in Search with a snippet before it can show up in AI Overviews or AI Mode. Meeting that floor promises nothing. Missing it removes the possibility.

How to tell it is done: open the rendered page and a text-only view side by side. The main answer, the headings, the lists, the tables, and the important qualifiers are all present in text.

Where people go wrong: treating schema as a citation switch, hiding the answer in an image, or blocking a crawler while trying to protect the site. An llms.txt file will not fix a structural problem. Fix the page.

Step 8: Run an extraction QA pass and measure what changed

Before you publish, run eight quick tests. Most take a minute each.

1. Outline test: read the headings alone. The answer path should be obvious.
2. Opening test: read the intro plus the first sentence under each heading. That should be a useful summary, not a set of teasers.
3. Copy-out test: copy each heading with its opening passage. It should still make sense alone.
4. Chunk test: one dominant question per section, explicit subjects, local qualifiers, no dangling references.
5. Format test: every list and table has a lead-in, labels, and a clear relationship to the answer.
6. Link test: descriptive anchors, relevant sources, links beside the claims they extend.
7. Text test: nothing important lives only in media or behind an interaction.
8. Technical test: crawlability, indexability, snippet controls, canonical, structured data, OAI-SearchBot.

After you publish, measure with the same prompts you used before the change. Record the date, the engine, the exact prompt wording, the page version, and whether the answer held a mention, a link, or neither. AI answers move between runs, so one response is a diagnostic sample, not proof. That is normal, and it is why you keep the baseline.

Bing's AI Performance report gives you vocabulary for this review: total citations, unique cited pages, page-level citation activity, and the grounding queries a cited page appears for. It is careful to say citation activity shows what was cited, not ranking or authority. Google Search Console folds AI-feature traffic into the Web search type, which is a traffic number, not a citation rate.

If you want this monitored rather than remembered, DeepSmith's AEO module runs your prompt set on a schedule and reports Mention Rate, Citation Rate, Share of Voice, Sentiment, and Visibility Trend, with the pages and competitor pages behind each. Engine coverage rises with the plan: Pro tracks ChatGPT, Grow adds Perplexity, Scale adds Gemini, and Enterprise covers all ten engines.

How to tell it is done: the page passes all eight pre-publish tests, the prompt baseline and page version are written down somewhere you will find them, and there is a review date on the calendar.

Where people go wrong: checking a keyword position and calling it an AI result. Confusing a brand mention with a linked citation. Declaring victory after one prompt run. When something looks off, diagnose in order: eligibility and crawlability, question match, answer clarity, chunk context, links and evidence, then competitor coverage.

A section card you can hand to a writer

Copy this into your brief so the person doing the rework has a pass or fail target per section:

Question or task owned by this section:
Heading:
Direct answer in the opening sentence:
Supporting explanation:
Steps, criteria, example, list, or table:
Condition or exception that must stay adjacent:
Relevant internal link:
Relevant source link:
Copy-out test result:

At the page level, a short work order does the same job. Restructure page for AI extraction one section at a time, and label the ticket refresh structure AEO so the next person knows this was shape work and not a fact update. Teams running this monthly keep a standing refresh structure AEO queue and pull from the top.

Primary question:
Primary answer:
Related questions:
Old section holding each answer:
New heading that will own each answer:
Structural action per section:
Technical checks:
Baseline prompts and review date:

There is no universal word count for a chunk, no magic number of headings, no required number of links. Pick the smallest unit that keeps the answer and its qualification together.

What to do next

Take one page. Not the whole back catalogue, and not the twelve your spreadsheet says are decaying. One.

Run steps 1 through 3 today and stop there. If the outline reads clean on its own, the hard part is behind you, because most of the value in an AI extraction refresh comes from the map and the headings. Publish the revision, write down the prompt baseline, and put a review date thirty days out.

Then do the next one. When you rework old post for AI extraction on a steady cadence, momentum matters more than a perfect first attempt.

If the tracking side keeps slipping, DeepSmith runs prompt monitoring and production in one place, so the questions you restructured around are the questions you measure against. You can start a free trial and see real data on your own pages before you pay, with no long-term contract.