AI search in 2026: how to check your site and plan improvements

If you want to understand if your site is ready for AI search, start with three questions: are key pages accessible to search engines, do they contain answers to customer questions, and what data will you use to evaluate changes?

For the business owner: the article will help to set a task and choose the order of work. For the developer and SEO specialist: below is an analysis of bot access, rendering, markup, and a 26-point STARCODE diagnostic matrix.

If the technical foundation is already in order and the question is how neural networks describe the company, move on to brand promotion in neural networks guide. These tasks can be combined in a single plan.

1. How AI search finds sources

One of the common mechanics of AI search is RAG (Retrieval-Augmented Generation): the answer is built based on the information found. The process differs across platforms, modes, and queries. Simply put, it is useful to distinguish the following stages:

1. Context search: the system can refine the query and perform related searches — this is called query fan-out.
2. Obtaining sources: a search index, available pages, and other supported sources are used. This does not mean that every site is necessarily loaded from scratch for every answer.
3. Preparation of the answer: the model compares information and may show links to pages used. The composition of sources depends on the platform and the specific answer; generation errors are possible.

2. Why being in the organic top 3 does not guarantee citation

Practical question for the site owner: «Our site is in 1st–2nd place for a commercial query. Why is the site from 7th place quoted in the Alisa AI or Google AI Mode block?»

Page position and link in a specific AI answer describe different observable results:

• Standard search results: shows pages that the system considers relevant to the query.
• AI answer sources: can relate to several aspects of a question and related searches. A high position is useful but does not guarantee the inclusion of a page in every answer.

Tutorial analysis: page accessible to a human, not a bot

Let's look at a hypothetical service site: the page opens in a browser, but a verified search bot receives an HTTP 403. This is an example of diagnosing a technical barrier, not a client project result.

1. Fix the observation: save URL, date, HTTP response, and WAF log. Confirm the bot by official signs; one User-Agent string is not enough.
2. Find the cause: match the query with the protection rule. Find out if one page, a group of URLs, or the entire site is affected.
3. Prepare a fix: describe a specific change, the responsible person, and the acceptance criteria. Save the protection of other queries.
4. Check result: the repeat query receives HTTP 200 and the main text. The report separately notes the fixed access and subsequent visibility indicators.

Such an analysis turns into a concrete TOR for an administrator. See a fragment of a demo report, to see how we identify the issue, implement a fix, and verify the result.

3. Technical accessibility: HTML, JavaScript and rendering

Robot capabilities differ. Google processes JavaScript, if the necessary resources are available. Therefore, a site built on React or Vue is not inherently excluded from indexing or AI answers.

During a technical audit, we compare the source HTML, the rendered page, and the text accessible to the robot. If the main content cannot be retrieved, we check resource loading, errors, and platform limitations. We choose SSR, SSG, or prerendering based on the discovered problem, not as a universal mandatory requirement for AI search.

For Google, check separately Search generative AI in Search Console settings. Site participation is enabled by default, but the setting can be changed or inherited from the parent resource. An excluded site does not participate in supported AI features; availability for regular search does not confirm this.

4. 2026 crawler map: who to allow and who to block

Policies for access to search, training, and user queries must be considered separately. Before changing robots.txt, check the current site rules and official OpenAI robot description.

• Search perimeter: Googlebot, YandexBot, Bingbot, OAI-SearchBot, and PerplexityBot belong to different platforms. Do not call their entire crawl “real-time loading”. OAI-SearchBot serves ChatGPT search; PerplexityBot is described in Perplexity help.
• Training and individual products: GPTBot relates to OpenAI model training. Google-Extended manages content usage for training and grounding in certain Gemini/Vertex AI products; this is not a separate HTTP User-Agent and is not Google Search management.
• User-initiated requests: ChatGPT-User can visit a page to answer a user request and perform actions in supported tools. Its visit does not mean indexing or training; rules for automatic crawling cannot be unconditionally transferred to this scenario.

# Explicitly move all applicable site restrictions to a dedicated group.
# These paths are provided as an example only.
User-agent: OAI-SearchBot
User-agent: PerplexityBot
Disallow: /account/
Disallow: /admin/

# Owner's specific decision: prohibition of automated collection for training.
User-agent: GPTBot
Disallow: /

5. WAF barriers, Cloudflare and timeouts: the hidden cause of drop-offs

403/429 errors, interactive checks, and an unstable server may hinder automatic access. We check request logs, official robot authentication signs, and the page loading result.

We check response time under specific project conditions: under load and during targeted bot requests. Using logs, we find the bottleneck, fix it, and repeat the measurement. We do not assign a general timeout for all RAG systems: there is no published universal value.

6. Footnote-friendly content: how to write for neural networks

Content should help a person make a decision. A good structure facilitates reading and information verification, but special 'chunking' and a fixed word count are not universal citation rules.

1. Answer the question: start the section with the essence if it is convenient for the reader; then explain the conditions and limitations.
2. Keep the context: indicate who the information refers to, the date it is relevant to, and in what cases it is applicable.
3. Check the evidence: publish real calculations, sources, and the methodology of your own measurements. Do not add numbers for the sake of apparent expertise.

7. Schema.org, FAQ and llms.txt: what works and what has become a myth

Structured data is useful for describing content and specific search features. Choose applicable Schema.org types and properties, check syntax and alignment with visible text. The presence of JSON-LD does not eliminate generation errors and does not guarantee an AI link.

Google does not require special AI markup or an llms.txt file. For other tools, check their own documentation and the specific benefit of such a file.

FAQPage must describe visible questions and answers. At the same time, Google disabled FAQ snippets on May 7, 2026. Using this type of Schema.org does not mean that an expanded FAQ will be shown in search results, nor is it a condition for AI citation.

8. Official 2026 analytics: Yandex, Google, Bing

Different tools have different metrics. In the report, separate platform data, your own control sample, and web analytics of traffic:

• Yandex Webmaster: “Visibility of the site in Alisa AI” shows SoV and examples of sources/queries. Clicks and impressions for this tool are not included.
• Google Search Console: Generative AI performance report contains page impressions in supported AI features and breakdowns by pages, countries, devices, and dates. Do not attribute individual AI clicks, CTR, or average footnote position to this report.
• Bing Webmaster Tools: AI Performance shows citations on supported AI surfaces and specific data about sources; this is not a measurement of the brand's position in all answers.

Evaluate conversions and leads separately by available referrers and site events. A missing referrer, closed assistant interface, and personalization limit the completeness of attribution. A robot request in the logs confirms access, not a recommendation to the user.

9. Engineering diagnostic matrix: 26 check points

Below is the STARCODE work matrix. This is a list of diagnostic questions, not mandatory rules for all AI platforms. For each project, we fix the applicability of the item and the acceptance criterion.

10. What to do after the check

Collect found issues into a plan: confirmation, priority, responsible person, and acceptance criteria. First, fix access errors and missing information on key pages, then proceed to expanding materials and regular measurements.

For sites where you need to separately design the actions of AI assistants, the following material is useful Agent View. Such a task is evaluated based on specific user scenarios after checking the site's basic accessibility.