How is a site prepared for AI search?
AI answers are assembled from the same index that produces ordinary results. Preparation is therefore not a special markup but a set of practices that make a page extractable: an answer in the first screen, a table instead of a paragraph, a claim with a source and a date, and structured data only for what is actually visible.
This page does not describe someone else's framework. It describes practices carried out on this site and on two taxi projects, with one measured outcome and an explicit list of what must not be concluded from it.
How is an AI answer assembled?
The system takes a query, expands it into a set of related questions, searches the index for each of them, selects passages from several documents, and assembles an answer with links to sources. A page enters that answer as a passage, not as a whole document.
The consequence is practical: the unit being selected is not the page but a paragraph, a table, or a table row. A page that gives a clear, self-contained answer in one place offers that unit. A page that spreads the same answer across an introduction, context and conclusion offers nothing that can be lifted out on its own.
- Query — the user asks a question.
- Expansion — one query becomes a set of sub-questions.
- Search — each sub-question is run against the same index that ordinary search uses.
- Passage selection — individual paragraphs or table rows are chosen from several documents.
- Answer — the text is assembled with links to sources.
Every expanded question needs a matching component on the page. If a component is missing for one of them, that is a functional gap, not a stylistic omission. How those intents are listed before writing is set out in the guide on query templates.
Why is there no special markup for AI answers?
Google points to the same basic practices that apply to ordinary search: availability for indexing, useful content and compliance with Search guidelines. There is no structured data type that guarantees entry into an AI answer.
Structured data still does its job — it describes machine-readably what is visible on the page and grants eligibility for certain result types. Eligibility is neither a guarantee of display nor a documented ranking effect. The rules for a cohesive graph are set out on the page about structured data.
What is not claimed here: no schema type guarantees a citation in an AI answer. Any offer promising “AI Overview optimisation” through added markup is selling something that is not documented.
Which components make a page extractable?
A component is extractable when it carries the whole answer without the rest of the page. A fare table, a sentence with a figure directly under the heading, and a row holding a single attribute all meet that condition. An introduction, a transition and a conclusion do not.
| Component | Answers the intent | Check |
|---|---|---|
| Answer in the first screen | What the answer to the heading is | The first few sentences carry the whole answer without context |
Table with caption and scope | Which values apply and to what | A single row is understandable without the rest of the table |
| Claim with source and date | Where the figure comes from and when it held | Every figure carries a period, a tool and a data owner |
| Heading as a question | Whether the page answers the query at all | The first sentence below the heading answers that heading |
Figures with alt and figcaption | What the diagram claims | The result can be read without the image |
Cohesive @graph | Which entities are on the page and how they relate | The markup describes only what is visible |
| Caveat next to an estimate | What must not be concluded from the figure | For every claim there is a conceivable proof that it is wrong |
Why the layout of a page by itself changes which part gets extracted is set out on the page about the main content.
What of that is applied on this site?
The following holds across the 28 pages of the Serbian version. The figures were counted in the site's source code, not estimated.
| Practice | Pages | Share |
|---|---|---|
| Section heading phrased as a question | 28 / 28 | 100% |
Figures with alt and figcaption | 16 / 16 | 100% |
Table with caption and scope | 25 / 28 | 89% |
| Answer in the first screen | 22 / 28 | 79% |
| Caveat next to a claim | 19 / 28 | 68% |
Date in a <time> element | 15 / 28 | 54% |
| FAQ block where an FAQ actually stands | 14 / 28 | 50% |
The last two rows are not shortcomings. An FAQ block stands on 14 pages and exactly 14 pages carry FAQPage in their structured data — not one more. Marking up an FAQ where none is present on the page would be a mismatch between markup and content.
How this was counted: by searching the source code of every page in the Serbian version for the presence of the corresponding HTML elements and classes. This is the state of the site, not a performance measurement. Coverage changes with every content edit.
What does our own measurement show?
On the taxi.co.rs project, pages whose main content is a fare table enter AI answers in 29.1% of their impressions. The site-wide average is 13.5%. City hubs, whose main content is an overview rather than a concrete figure, stand at 6.8%.
- City fare pages — 29.1% of impressions; main content is a fare table.
- Site-wide average — 13.5% of impressions.
- City hubs — 6.8% of impressions; main content is an overview, not a concrete figure.
Measurement: Google Search Console, generative features report, – , aggregated by page, owner data.
What this figure is, and what it is not: the difference between page types is measured and repeats on the English version of the same pattern. What is not measured is the cause. Content, layout, internal links and structured data were changed at the same time on that project, so the difference is attributed to the system rather than to any single component. A control case on another project produces a markedly weaker result and is described in the leskovac.taxi study.
The full distribution, with every page type and the complete list of limitations, is in the taxi.co.rs case study.
What about AI search can be measured, and what cannot?
What is measured is how many times a link to the site was shown in Google's generative features, and how many visits arrive from AI platforms. What is not measured is which queries caused them, or how many times the site was cited without a click.
| Source | Gives | Does not give |
|---|---|---|
| Search Console, generative features | Impressions per page | Queries, clicks as a separate metric, export through the API |
| Analytics, referrer from AI platforms | Visits that arrived | Citations without a click, visits with no referrer |
| Server and CDN logs | Visits by AI crawlers | The link between a crawl and a citation |
Because of this, visibility in AI answers cannot be expressed as a single number. Every measured figure is a lower bound: visits from mobile apps often carry no referrer and end up counted as direct traffic, and a citation without a click leaves no trace in analytics.
How the AI impressions report is read and where its limits lie is set out on the page about measuring results.
Limit of the comparison: Search Console data and AI platform visit data do not share a definition of the event or a period. Impressions and visits are neither added together nor divided by one another.
How do you check your own page?
Take one page and work through eight questions. Each has a yes or no answer, with no grade in between.
- Does the first sentence below the heading answer the question in that heading?
- Can that sentence be read on its own, without the rest of the page, and still make sense?
- Is the most important figure in a table rather than in a paragraph?
- Does every table have a
captionandscope? - Does every figure carry a period, a tool and a data owner?
- Can the result of every diagram be read without looking at the image?
- Does the structured data describe only what is visible on the page?
- For every claim — is there a conceivable proof that it is wrong?
A page that fails the first two questions has nothing to offer at the passage selection step, however good the rest of it is. What this check looks like applied to a whole site is shown in the audit example.
Common questions about AI search
Is there a special schema markup for AI answers?
No. Google points to the same basic practices: availability for indexing, useful content and compliance with Search guidelines. There is no “AI Overview schema” that guarantees a citation.
Can I see the queries behind AI impressions?
Not through an official report. Search Console reports impressions in generative features but not the queries behind them. The data is exposed neither through the API nor through the BigQuery export.
How is visibility in ChatGPT measured?
Only the visits that arrive are measured, through the referrer in analytics. A citation without a click leaves no trace, and mobile app traffic often carries no referrer, so every measured figure is a lower bound.
Should AI crawlers be blocked?
It depends on the business model. Blocking protects content from being used in training, but it also removes the possibility of being cited. The decision is made per domain, not as a general rule.
Is this different from ordinary SEO?
Not fundamentally. The same index, the same guidelines, the same quality criteria. The difference is that a passage is selected rather than a document, so layout and extractability carry more weight than before.
Does shorter text stand a better chance of being cited?
It is not length but extractability. A sentence carrying a concrete figure directly under the heading can be lifted out without the rest of the text. The same figure spread across three paragraphs cannot.
Primary sources
This guide is a method. How it is carried out on a real site, with a price and a timeline, is shown by the semantic SEO audit.
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