AI and SEO involve two separate operational jobs: keep important pages eligible for conventional search, then measure Google Search performance and third-party AI observations using the data each channel actually provides. If a product guide has an accidental noindex directive, fix that before rewriting the page to pursue an AI citation.
Google says AI Overviews and AI Mode have no additional technical requirements beyond existing Search eligibility. AEO, or answer engine optimization, and GEO, or generative engine optimization, are industry terms rather than separate Google-required technical checklists. Eligibility does not guarantee crawling, indexing, ranking, inclusion, citation, visits, or conversions.
This guide turns that distinction into a practical operating model. It covers a two-gate content and technical review, a Search Console measurement design, separate OpenAI crawler policies, and a proposed method for observing third-party AI answers. The implementation patterns are recommendations, not vendor-provided workflows or guarantees.
Check eligibility first, make the page distinctly useful second, and measure each search channel at the grain its data supports.
What should SEO teams do differently for AI search?
Start with a two-gate decision. If a page fails a technical eligibility check, fix access, indexing, or snippet controls before editorial rework. If it passes, ask whether it adds first-hand experience, evidence, or useful analysis beyond a generic summary.
Google’s guidance for AI Overviews and AI Mode says pages must be indexed and eligible to appear with a snippet in Search to be eligible as supporting links. Google also says there are no additional technical requirements specifically for these features. Foundational SEO and a helpful, reliable page remain the practical starting point for Google.
Keep three outcomes distinct: Google Search performance, site engagement and conversions, and observations made in other AI products. Search Console does not provide a complete event-by-event record of AI citations, and a result observed in a third-party product is not a Google Search metric.
Gate one: confirm that important pages are eligible for Google Search
Audit the deployed page version, not only a draft or CMS preview. Record the URL, page version, audit date, expected indexing and snippet use, results, and owner responsible for fixing exceptions.
Check the response status and headers, robots.txt, hosting or CDN access, page-level indexing and snippet directives, canonical signals, internal links, rendered text, and structured data. Important content should be available in text and discoverable through internal links. A 200 response alone does not establish indexability.
Google’s robots meta tag documentation explains that nosnippet prevents content from being used as a direct input for AI Overviews and AI Mode. max-snippet limits text Google may use. These controls must be discoverable by Googlebot, so a robots.txt block can prevent Google from reading page-level directives. The appropriate setting depends on the publisher’s intent.
If JavaScript renders key content or directives, inspect the rendered page and investigate any mismatch rather than assuming that source HTML tells the whole story. Check that the canonical points to the intended URL and that internal links support the same destination.
Use structured data only for a supported Search feature and only when it accurately represents visible page content. Google’s structured data policies make clear that valid markup can establish eligibility but does not guarantee display. It is not a special AI-citation mechanism.
- Access: the expected response is returned and Googlebot is not blocked by robots.txt, authentication, CDN, WAF, or other infrastructure. Route failures to web operations.
- Directives: indexing and snippet controls match publisher intent. Have the SEO owner verify changes before deployment.
- Discovery: the canonical and internal links point toward the intended page. Resolve conflicting signals with the SEO or site owner.
- Rendered content: important content appears in the rendered page text. Send rendering or content-availability failures to web operations.
- Structured data: markup parses and matches visible content. A parser failure or content mismatch requires editor or developer action.
- Version control: the audit is attached to the deployed page version and repeated after a fix goes live.
A small audit record can keep the result actionable. The following is an illustrative output contract, not a Google schema. Use the field names and allowed values that fit the receiving issue tracker or QA system.
{
"url": "https://example.com/guides/topic/",
"http_status": 200,
"robots_allowed": true,
"meta_robots": "index, follow",
"canonical_url": "https://example.com/guides/topic/",
"important_text_rendered": true,
"structured_data_parse_status": "pass",
"visible_data_match": "pass",
"page_version": "2026-10-10.2",
"review_owner": "seo-team"
}Use deterministic rules for response codes, directives, parsing, required fields, and deduplication. AI can flag unclear visible content or possible mismatches for human inspection, but it should not decide that a hard technical check passed. Store the record against the deployed URL and page version, then route exceptions to the owner who can change the failing system.
Gate two: invest in value that is not a generic answer
When technical eligibility is sound, identify why a reader would choose this page over a generic summary. Useful improvements include first-hand experience, original research, a clear method, expert interpretation, worked examples, and decision criteria that help someone take the next step.
For each proposed update, record the reader decision it supports, the evidence or experience that makes the answer distinctive, and the reviewer accountable for checking it. If the team cannot identify distinct value, improve the reporting or usefulness rather than generating more near-identical query pages.
Google’s guide to generative AI Search documents no special AI markup, required chunking format, or ideal page length. Organize material clearly for people. Use structured data for a supported feature when appropriate, not because it is assumed to trigger an AI citation. Google also says llms.txt does not help or harm Search visibility.
AI can assist with bounded tasks such as extracting claims that need sources, suggesting unanswered reader questions, or identifying repetitive sections. A named human editor should verify factual claims, source context, and interpretations before publication. Teams considering governed task automation can review AI agent implementation. That link is a general service resource, not an SEO ranking or citation product.
Measure Google Search performance without inventing an AI-citation metric
Google says traffic from its AI features is included in the overall Web search type in Search Console. The Search Analytics API returns grouped performance rows, not a raw feed of individual AI Overview impressions or citations. Supported dimensions can include date, page, query, country, device, and search appearance where available.
For a scheduled or analyst-led pull, use the authorized Search Console property and preserve the request’s date range, dimensions, filters, aggregation type, and data state alongside returned rows. A useful proposed warehouse grain is property, date, page, query, country, device, search appearance, and aggregation type. This is a storage design, not a Google-issued record identifier.
The Search Analytics API reference documents row limits and incomplete-data behavior. Results may be bounded, and fresh data may be incomplete. A missing row is not proof of zero activity, especially when the query did not request a relevant dimension or the response is limited to top rows.
Where concurrent jobs may write the same aggregate, define a database uniqueness constraint over the actual dimensions and use an atomic upsert. A lookup followed by an insert can race. Keep extraction run IDs outside the aggregate key so repeated pulls remain auditable without creating duplicate metric rows.
Treat ChatGPT search access as a separate crawler policy
OpenAI distinguishes OAI-SearchBot, used to surface websites in ChatGPT search, from GPTBot, used to crawl content that may be used to improve OpenAI’s foundation models. The controls are independent. Allowing the search crawler does not require allowing the training crawler, and neither setting controls Google AI Overviews.
A publisher that wants to permit ChatGPT search crawling but disallow GPTBot could use rules like these, after confirming the policy with its web or security owner:
User-agent: OAI-SearchBot
Allow: /
User-agent: GPTBot
Disallow: /This is an illustrative robots.txt policy, not a confidentiality control. Test permitted paths against robots.txt, authentication, CDN and WAF rules, firewall settings, bot mitigation, and server logs. If IP allowlisting is used, compare observed requests with OpenAI’s published crawler IP ranges in its crawler documentation. Record the decision separately for each user agent and content area.
The publisher or legal and security owners decide whether training-related crawling is acceptable. Web operations investigates access failures. OpenAI recommends allowing OAI-SearchBot if a publisher wants its content considered for ChatGPT search, but access does not guarantee inclusion or citation. Its ChatGPT search guidance also advises checking citations against their sources.
Build an AI visibility measure that can be reproduced
There is no universal cross-engine citation-reporting API in the official guidance reviewed here. A controlled prompt test can still help a team observe a defined set of questions, provided it is labeled as the team’s proposed measurement design rather than an official Google or OpenAI workflow.
Keep separate records for separate things:
- Prompt run: one prompt submitted to one product or engine, in one locale, at one time. Store a unique run ID, prompt ID, prompt-set version, product, exposed model variant if known, locale, timestamp, and response record or permitted excerpt.
- Citation: one cited URL in that particular response. Store it as a child record linked to the run, with a citation ordinal and canonicalized URL.
- Entity mention: one classification of whether a defined entity appears in that response. Store it separately from citations because a mention may have no citation and a citation may not name the entity.
- Period summary: a calculated aggregate across a defined period and prompt set. Include the product, locale, prompt-set version, sample size, citation definition, and reporting window.
One prompt run is an observation, citations are child records, and a share-of-voice rate is an aggregate over a defined sample. If one answer contains three citations, record three citation rows linked to the run. Do not place an ambiguous citation count in a summary row and treat it as an event.
For reliable retries, assign a new run ID to each genuinely new test. A retry of the same logical run can reuse its run ID. Enforce uniqueness in the database, such as a unique run ID for prompt-run records and a unique combination of run ID and citation ordinal for citations, then use transactional upserts. Do not rely on lookup-then-create logic when runs can overlap.
Fix the prompt set, product, locale, observation window, and citation definition before comparing periods. Preserve an unavailable model or version as unknown rather than guessing. If the product changes, the prompt set changes, or an observation is corrected, label the break in the report. A measurement owner should approve the sample, and a human reviewer should resolve uncertain citations or entity matches.
A practical order of operations for an AI-era SEO program
- Technical eligibility: SEO owns indexing intent; web operations owns crawl, rendering, CDN, or WAF failures. Store URL-level checks against the deployed page version.
- Evidence and usefulness: content owners identify the reader decision, distinctive evidence, and claims requiring verification. An accountable editor approves publication.
- Search measurement: analytics or data engineering owns Search Console authorization, request grain, extraction metadata, and warehouse uniqueness. SEO interprets trends with the stated data limits.
- Separate AI observations: a search insights owner approves prompts and definitions, then stores run, citation, and mention records separately. Human reviewers handle ambiguous classifications.
- Review results and exceptions: report audit pass rates, updates with verified sources, Search Console trends, site-defined conversions, and consistently sampled AI observations without promising ranking gains.
Use rules for response codes, robots directives, required fields, parsing, and deduplication. Use AI for bounded suggestions or classification that a responsible reviewer can verify. Escalate sensitive claims, original research interpretation, pricing, legal or regulated content, and consequential publication decisions to people with the relevant authority.
Frequently asked questions about AI and SEO
Does Google require special GEO or AEO markup?
No. Google documents no additional AI-specific technical requirements or special AI markup for AI Overviews and AI Mode. Use structured data when it accurately supports a documented Search feature.
Is llms.txt required for Google AI Search?
No. Google says llms.txt does not help or harm Google Search visibility.
Does structured data guarantee an AI citation or rich result?
No. Valid structured data can establish eligibility for supported Search features, but it does not guarantee display or an AI citation.
Does Search Console provide a complete AI-citation database?
No. Search Console reports grouped Search performance and its API has limits. It is not a raw, per-citation event feed.
Does allowing OAI-SearchBot also allow GPTBot?
No. OpenAI documents separate controls for ChatGPT search crawling and potential model-training crawling. A publisher can allow one and disallow the other.