Search engines based on language models (LLMs) fundamentally change the way content from the web is discovered and presented to users. LLM indexing refers to the process in which large language models acquire, process, and “understand” content from websites – in a way that is radically different from traditional search engines. Understanding how an LLM indexes content is crucial for SEO/AEO specialists. In the era of Answer Engine Optimization (AEO), it is no longer just about ranking blue links, but about ensuring that our content will be found and used by the artificial intelligence that generates answers. This article thoroughly analyzes the anatomy of crawling in the AI era, showing the differences between classical search indexing and the “embedding indexing” used by LLMs. You will learn why existing SEO practices are not enough to achieve visibility in generative AI systems, and what exactly to do so that your content powers answers provided by models such as ChatGPT, Claude, or Google Gemini.

Conceptual foundations

A Large Language Model (LLM) is an advanced neural network trained on massive text datasets, capable of generating answers and carrying out conversations. Unlike a traditional search engine, which returns a list of websites matching the query, an LLM can create direct, conversational answers, often combining information from multiple sources at once. This gives rise to new concepts:

Crawling vs. “AI crawling”: Search engines such as Google use robots (e.g. Googlebot) to crawl the web – visiting links, downloading HTML code, rendering JavaScript and collecting content for the index. An AI crawler performs a similar function for LLMs, but its goal is to feed the system with knowledge rather than build a public link database. Importantly, not all LLM bots work the same way: Googlebot still powers Google Search and indirectly SGE (Search Generative Experience), whereas e.g. OpenAI’s GPTBot or PerplexityBot are new players crawling the web with a view to training models or providing them with data on demand. For an SEO specialist, this means that you need to ensure access for various AI robots, not only traditional search crawlers.

Document indexing vs. semantic indexing: A classical search engine builds a document index – each page is a unit analyzed in terms of keywords, backlinks, and more than 200 ranking factors. An LLM, on the other hand, creates a semantic index. It does not store pages as a whole; instead, it splits content into small semantic “pieces” (English: chunks) and remembers the meaning of these fragments in the form of numeric vectors. In other words: Google indexes pages and words, while an AI model indexes the meanings of sentences and paragraphs. This is a fundamental difference – an LLM does not care about an exact keyword match, but about whether a fragment of your content semantically matches the user’s question.

Embedding and vector knowledge store: An embedding is a representation of text (or other information, e.g. an image) in the form of a vector – a list of several hundred numbers that reflect context and meaning. The process of embedding indexing means that for each “piece” of content, the model generates a vector and stores it in a special vector database (a so-called vector store). Such a database allows you to very quickly search through vast amounts of information based on mathematical similarity: fragments with a similar meaning have vectors located close to each other in the space. When an LLM receives a query, it also converts it into a vector and searches the vector index for the most semantically similar content. Thanks to this, it can find an answer even if the words used do not match – what matters is similarity of meaning, not identical phrases.

Model memory vs. retrieval: It is worth distinguishing two sources of knowledge for an LLM. The first is the parametric memory of the model – the knowledge that the LLM acquired during training (e.g. ChatGPT has a large portion of the internet encoded in its weights up to 2021). This memory is static, however, and does not contain the latest information or full texts. The second source is the retrieval mechanism, i.e. extracting information from an external database (e.g. from the aforementioned vector index containing up-to-date web pages). Modern systems create a hybrid: the language model is supported by a search module that fetches current content and supplies it to the model during answer generation. This technique is called Retrieval-Augmented Generation (RAG) – the model generates an answer based on information pulled from a knowledge base in real time. For AEO specialists, this means that even the most intelligent LLM has to have something to draw fresh data from. If your site does not end up in such a collection (embedding store), the model may rely on incomplete or outdated training knowledge.

AEO and GEO: Answer Engine Optimization (AEO) is the practice of optimizing content for answer engines – e.g. voice assistants, AI chats, which directly provide the user with a concrete answer. The term has recently evolved towards GEO (Generative Engine Optimization), emphasizing the generative nature of new systems. The essence remains the same: the goal is to adapt the site so that it becomes the source of information used by AI. In traditional SEO we ask: “how to get a high position in the results?”. In AEO the question is: “how to ensure that content from our site is quoted and used in the answer generated by AI?”.

Technical anatomy of LLM indexing

Let’s look in detail at how the process of “indexing” content by an AI system works, comparing it step by step with the analogous stages in a classical search engine. The technical differences between search crawling and LLM indexing affect our optimization strategies.

1. Crawling – acquiring content

Traditional crawling: Googlebot and other bots crawl the web by following links. They send HTTP requests to servers, download the HTML code of pages, and often also render JavaScript (e.g. Google uses a Chromium-based browser engine for this). The crawler has a list of URLs to visit (coming from previous indexes, sitemaps, or links found on other pages) and systematically “walks” websites. At the same time, it respects the rules set in robots.txt – a file in which the webmaster can indicate which areas of the site to block from indexing. The result of crawling is the raw content of the page (text, metadata, HTML code), passed further to indexing.

Crawling in the world of LLMs: AI models do not have their own global search engine on the scale of Google, but rely on several approaches:

Use of existing indexes: Platforms such as Bing Chat or Google SGE are based on the indexes of their search engines. When a user asks an AI a question, the system refers to the traditional index, performs a series of queries (sometimes multiple parallel searches – so-called query fan-out) and fetches the necessary pages. These pages are then passed to the LLM for summarization. From the SEO point of view, this means that basic indexing by a search engine remains a prerequisite – your site must be indexed in Google/Bing in order to appear in AI Overview or AI Mode at all. Google confirms that you do not need to submit content to AI separately – if you meet the requirements of standard indexing (and do not block snippets), you can be used as a source for generated answers.

Independent AI crawlers: In parallel, new players have appeared. OpenAI GPTBot – launched in 2023 – is a bot that independently crawls public pages to provide data for model training (such as GPT-4) or possibly their later refreshing. PerplexityBot operates for the Perplexity AI search engine – it maps pages to create its smaller, curated index. There are also others, e.g. Anthropic (Claude) bots or unofficial scripts indexing for various solutions. Some of them do not render full JS and do not wait long for loading – PerplexityBot, according to Daydream’s analysis, does not execute JavaScript at all, fetching only static HTML. In practice: if critical content on the page loads only on the client side (e.g. via React or AJAX), it may escape the attention of such a bot. Likewise, pages behind paywalls, requiring login, or protected by aggressive anti-bot mechanisms (Cloudflare, IP blocks) may be omitted by the LLM index. The AI crawler is looking for easy prey – public pages, fast to fetch, not causing technical problems.

Curated collections and external data: Not all LLM data come from raw web crawling. Large models are often trained on collections such as Common Crawl (a public snapshot of the web), licensed datasets (e.g. books, knowledge bases), social data (e.g. Wikipedia). Moreover, when AI generates an answer, it may use external APIs (e.g. databases, knowledge services) that provide information directly. For a site owner this means that it may be valuable not only to be “in Google”, but also to be present in various knowledge bases such as Wikidata, or to use schema.org to provide structured data understandable to different engines.

In summary, crawling in the AI era is a more diverse ecosystem: traditional indexing + new independent bots + on-demand queries + integration with knowledge bases. Your goal is to ensure access for all of the above:

Example robots.txt snippet opening a site to AI bots:

User-agent: GPTBot
Allow: /
User-agent: PerplexityBot
Allow: /
User-agent: Googlebot
Allow: /

The above rules in robots.txt give full access respectively to: OpenAI’s official bot, Perplexity’s bot and (as a reminder) Googlebot. It is worth periodically updating this file with new user-agent identifiers as new AI appear. If you use firewalls like Cloudflare, add exceptions for recognized bots so they do not have to pass Captchas.

2. Cleaning and preprocessing

When the crawler fetches a page, the content preprocessing phase begins. Google and other search engines parse HTML: they recognize the , – tags, paragraph content, links, images (and their alt attributes), scripts, etc. Duplicate sections are removed (e.g. repeated menus, footers), canonical addresses are detected, meta tags are processed (e.g. , meta-robots with noindex, etc.). The result is a model of the page structure and clean text ready for indexing.

In the case of LLM indexing, the role of this stage is even more important. AI models want to receive clear, understandable fragments. The system therefore removes “noise”: scripts, styles, navigation – everything that is not the main content. Additionally, text normalization is often applied: character fixes, replacing synonyms with unified forms, and above all, entity detection. Entities are all concrete concepts in the text (people, companies, products, dates, places). AI tries to identify them because they are key to understanding context and assigning trust weight. For example, if your site mentions “John Smith, CEO of OpenAI, stated in the NIST 2023 report…”, then a correctly identified entity OpenAI or the NIST 2023 report may later be used as a signal of credibility or linked with other data about OpenAI.

For you, the conclusion is as follows: simplify and structure content with this step in mind. The less clutter, the better. Avoid excessive DOM elements that can “blur” the main content. Use consistent naming (e.g. name the product or person uniformly across the site). Also remember to transfer important information from images or dynamic widgets into text – e.g. if an infographic contains important data, describe it in the text or in the image alt attribute, otherwise AI may not register it.

3. Chunking – dividing into semantic fragments

This is the heart of LLM indexing. After initial cleaning, the entire text of the page is divided into smaller pieces – chunks. It is important to understand what constitutes a chunk: it can be a single paragraph, a section with a heading and several paragraphs, a bulleted list item, a single FAQ question – in short, a logical thematic unit that can be understood independently of the rest.

A traditional search engine does not perform such explicit splitting – it indexes the entire page as a document (although it also extracts fragments matching the query to create snippets). An LLM indexer, however, necessarily cuts content into pieces because the language model has a limited context window – it cannot take 50 thousand characters at once. Instead, later, when a query comes, it will select only a few most relevant chunks.

What determines chunk boundaries? To a large extent, the HTML structure and semantics of the page. If your page is well organized:

It uses , … headings hierarchically to divide topics,

It has clearly separated paragraphs, lists, tables,

It contains FAQ sections, quotes, etc.,

then the chunking algorithm will very likely cut the content at those places. For example, each heading followed by text can become the start of a new chunk. Conversely, a messy structure (e.g. skipped heading levels, chaotic mixing of topics) will cause chunk boundaries to be random and may split information that should stay together.

Let’s imagine a guide page:

Such a structure – with clear headings and lists – makes it easy to separate logical parts: a separate fragment about “Understanding LLM Crawling”, a separate “Key Best Practices” list, etc. If the same text were one long block without headings, the model would have trouble splitting it sensibly, and important points could “disappear” in a large fragment.

Chunking and AI visibility: Well-separated chunks increase the chance that exactly that fragment will be selected in the answer. If, for example, a user asks: “What is the difference between AI crawling and traditional crawling?”, the LLM will not provide the entire article – it will try to find a single fragment explaining the difference. If such a meaningful paragraph/section exists (e.g. “Understanding LLM Crawling”), it has a higher chance of being matched. Conversely, if information is scattered throughout the text, the model may fail to connect it or return something less precise. In practice: every important topic or question on your site should have its own “independent” fragment – e.g. in the form of a paragraph with a clear topic, or a question followed by an answer (see: FAQ).

4. Creating embeddings (vectorization)

Each extracted chunk of text is transformed from text into a vector of numbers, i.e. an embedding. This task is performed by a separate model (a so-called embedding model), typically a neural network trained so that semantically similar texts have similar vectors. To illustrate, an embedding model transforms a sentence like “Jak działa indeksowanie LLM?” / “How does LLM indexing work?” into a vector: [0.12, -0.45, 0.78, …] (the number of elements may be 384, 768, even 1536 – depending on architecture). This mathematical record of “meaning” allows the system later to quickly compare a query with candidate answers.

Why are embeddings key? Because traditional search is based on matching words, while an LLM is based on matching meanings. An embedding encodes context – it “understands” that “LLM indexing” is close to the concepts “vector database”, “embedding store” or “semantic search”, even if the words differ. This allows AI to go beyond the limitations of keywords. For you as a content creator, this means that writing style and substantive quality affect embedding quality. As GEO specialists have noted, embeddings from “flat” text full of generalities will be less distinctive, making it harder for them to compete in vector space. In contrast, content that is concrete, full of facts and a unique take on the topic will generate vectors the model regards as distinctive. This increases the chance of being among the top closest vectors for a given query.

In other words: when writing for AI, write precisely and substantively. Avoid fluff – otherwise your fragment will blend embedding-wise with thousands of similar generalities and may be overlooked. Include conceptual keywords (important entities, terms) – the embedding will “pick them up”. For example, instead of the generic “Our company creates innovative solutions”, it is better to write “Our company XYZ specializes in natural language processing algorithms and won Award A for AI startups in 2023”. Such a fragment contains specific entities (XYZ, NLP algorithms, Award A 2023), which increase the informational density of the embedding and the model’s confidence in it.

5. Vector store – semantic index

In a traditional search engine, the result of indexing is adding the page to the index database – in Google’s case, this is a massive system storing billions of pages, with information on which words appear on which pages (inverted index) and lots of metadata (PageRank, link data, etc.). In an LLM system, the analogue is a vector database, which stores embeddings of all chunks along with pointers to the source (e.g. the URL of the page they come from, title, etc.). Popular vector databases (such as Pinecone, Weaviate or Vespa) are optimized for so-called nearest neighbor search (ANN) – they can return N most similar vectors among millions in a fraction of a second.

Curated index vs. full index: It is worth noting that not every piece of content ends up in such an index. While Google tries to index “the entire internet” (which is impossible at 100%, but it aims for maximum coverage), Perplexity, for example, uses a curated index – that is, deliberately limited to high-quality sources. Its creators indicate that they index only pages meeting certain criteria (clarity, authority, lack of spam). SGE may work similarly: AI Overviews more often cite expert sites and skip shallow aggregator content. Hence, once again: the quality and authority of your site matters even before the search itself – it may determine whether you are placed in the “AI memory” at all. So you need to care not only about individual pages, but also about the domain’s reputation (more on this later under trust signals).

6. Search and retrieval of information

With a built vector index, the model can use it at any time. Retrieval, i.e. extracting information, usually occurs when the user asks a question. It works like this:

Embedding the query: The user’s question (e.g. “How does an LLM index websites?”) is also converted into a vector by the same (or a similar) embedding model. This yields a mathematical representation of the user’s intention.

Vector matching: The system runs a vector query against the database – it looks for chunks whose embeddings are most similar to the query embedding. The result is a list of e.g. a dozen fragments from various sites, ordered by semantic similarity to the question.

Filtering and preselection: Often, initial filtering is applied. For example: eliminating fragments from suspicious domains, preferring fresher ones (if the question suggests the need for current info), taking language into account (so as not to mix languages when multiple are present in the index), or applying rules such as “max 2 fragments from one domain” for source diversity.

Trust signals and reranking: This is the key step that differentiates AI retrieval from plain search. If we have, say, 10 candidate fragments, the model evaluates them in terms of reliability and contextual fit. It takes into account, among other things, the fragment’s metadata:

What is the authority of the domain or author? (e.g. does the fragment come from a site recognized as an expert in this field).

Does the fragment contain specific facts, dates, quotations – which increases its value?

Does the page have schema markup that facilitates interpretation (e.g. is the fragment part of an FAQPage or an article with a designated author and date)?

How fresh is the fragment (publication or update date, if known)?

Does the content appear reliable (e.g. expert tone, no obvious errors)? — here, models may evaluate writing style or compare with other sources.

All these factors allow the LLM to choose, for example, between two semantically similar answers the one more trustworthy. If the question concerns health and we have a fragment from a forum and one from an official medical site, the expert fragment will be preferred. Trust signals in the LLM world are like a Google ranking equivalent, but instead of PageRank and links, E-E-A-T (Experience, Expertise, Authoritativeness, Trustworthiness) expressed in the content and its context matter.

Feeding the LLM with context: Ultimately, the selected fragments – usually a few (e.g. 3 to 5) – are passed to the language model’s context window as additional material. The model receives the user’s query plus these fragments as a “hint” and generates the final answer based on them.

It is worth noting: the model can also use its own memory. That is, if the question concerns something it already has in its parameters, the fragments will rather serve to confirm and provide sources than to supply all knowledge. But for new or detailed questions, it is the supplied chunks that will be the main content to transform into an answer.

7. Generating answers and citing sources

The final step takes place inside the LLM: based on the query and supplied fragments, the model synthesizes a new statement. It creates sentences in its own words, trying to answer the question precisely. This is a big difference from a search engine – instead of a list of links, we have a synthetic answer in natural language.

If the system is well designed (e.g. Bing Chat, Perplexity, Google SGE), it adds references to the generated answer – indicating which sources were used. Sometimes this is done via numbered footnotes, sometimes through a “Sources:” list with site names. For a site, this is the moment of truth: your site may be named and linked here as a source, even though the user has never visited it directly before. This is the new type of visibility in the LLM era – you can become part of the answer, building awareness of your brand or expertise, even if the user does not click on a search result.

It should be emphasized, however: different systems have different citation policies. Bing and Perplexity always try to show sources. Google SGE in AI Overview shows several links related to the answer, but does not always explicitly quote a sentence from your site – it rather suggests “see also these pages”. Some implementations (e.g. dedicated chatbots based on the OpenAI API) can generate answers with no explicit sources at all, which is problematic from the site’s perspective (your content may be used, but you don’t know about it). Therefore, all the more so, when caring about LLM indexing we operate somewhat in the dark – we must assume that even if the quote is not visible, the model still uses our content for a correct answer, and the user appreciates the higher quality of such an answer. In the ideal scenario, out of curiosity they will click the source to learn more – which generates traffic for you. In a less ideal scenario – at least your brand name is mentioned, which also has some value (e.g. builds a reputation as an expert in a given field).

Summarizing this section, key technical differences between traditional indexing and LLM:

Unit of index: the search engine indexes the page (URL), the LLM indexes individual chunks (paragraph/section).

Data structure: the search engine relies on a keyword and link index (inverted index, PageRank), the LLM – on a vector index and associated metadata.

Matching: the search engine looks for words, the LLM – looks for semantic similarities.

Ranking vs. retrieval: Google ranks hundreds of pages, the LLM retrieves a few fragments and does not “rank” them for display, but uses them for the answer. Retrieval is the new ranking – if a fragment is not retrieved, you do not appear at all.

Quality signals: Google heavily takes backlinks and overall domain authority into account in ranking, the LLM evaluates what is in the content – the author, mentions of awards, reviews, consistency with other known facts. Links as such are less important (although if your article is quoted by many others – that’s also a signal it contains valuable content).

Result presentation: in Google – links, meta descriptions, sometimes rich snippets. In an LLM – a smooth answer with an optional source reference. This changes the optimization approach: we no longer fight for the visibility of the page title, but for including our sentence in the AI answer.

Implementation guide step by step

So how do you prepare your site to meet the challenges of AI crawling and vector indexing? Below is a specific, practical action plan:

Ensure access for AI bots – make sure you are not blocking modern crawlers. In the robots.txt file, add rules allowing crawling for known agents such as GPTBot (OpenAI), PerplexityBot, and of course do not block standard Googlebot/Bingbot. If you use firewalls (Cloudflare, ModSecurity), configure exceptions for these agents or for the IP ranges of official bots. Also remember that some AI bots behave like ordinary user traffic (e.g. ChatGPT browser mode or Perplexity-User simulate a normal browser). Therefore, it’s better not to apply overzealous blocks against “unknown user-agents”, because you may accidentally cut off AI visits. On the other hand, consciously decide whether there is something you want to restrict: e.g. if you do not want your content used in model training, you can add User-agent: GPTBot Disallow: / (OpenAI and Google offer opt-out mechanisms, but this is a business decision – the cost will be the absence of your content in those AIs). Most sites focused on AEO should rather open the doors, not close them.

Develop a clear site structure – approach content modularly. Every page should have a logical hierarchy of headings and sections. Start with one (page title). Divide topics using for major subsections, optionally for further subpoints. Avoid skipping heading levels (e.g. from directly to without – this can confuse the parser). Make sure each heading is descriptive – not “Section 1” but e.g. “3. How crawling works in LLMs”. Underneath, stick to the topic indicated in the heading, do not mix it with other threads. Use bullet and numbered lists to enumerate multiple elements (a list is an ideal chunk – each point can be returned as an answer to a question like “list X factors…”). Add captions or descriptions to charts/images (even as a plain right under the image) – such a description can also be an independent chunk. In short: write with the assumption that each paragraph or list may be read on its own, without the context of the entire page.

Ensure accessibility-friendly content – what does accessibility have to do with LLMs? Quite a lot, as it turns out. Many accessibility principles overlap with what AI needs for correct chunking and content understanding:

Add alt attributes to images that convey their meaning. If, for example, you have an infographic “Anatomy of an LLM”, the alt might be: “Diagram of an LLM system: crawling -> chunking -> embedding -> retrieval.”. Such text not only helps visually impaired users, but also AI – it can understand what the image depicts and use that knowledge. Without alt text, the image may be ignored or interpreted by computer vision algorithms, which is less accurate.

Maintain correct heading order (as above) – this is also a WCAG guideline for heading navigation.

Use clear, descriptive link texts. From an accessibility standpoint, a link like “click here” is bad – the user doesn’t know where it leads. For AI it’s similar: “more” or “read more” say nothing, whereas a link reading “See our comparative test of LLM indexing” already carries context. The LLM can treat the link text as extra information about what’s on the linked page. Additionally, if another site links to you with the phrase “expert AEO guide”, your authority in that topic increases. In summary: create links that in themselves answer the question of what is there.

Avoid embedding text in non-text elements. Important content should not exist only in video, images without transcripts, or Flash animations (luckily, that last one is rare today). The POUR principle (Perceivable, Operable, Understandable, Robust) in accessibility essentially says: make life easier for the recipient. AI is a specific type of recipient, but it also appreciates a clean, perceivable message.

Place structured data (schema.org) – structured data is the language you speak directly to algorithms. For LLMs they are as valuable as for search engines. At the indexing level, if your page has, for example, an FAQPage designation with questions and answers, an AI crawler immediately knows this is question-answer content and can mark that fragment as high value (AI loves well-formatted questions and answers – in responses it often quotes FAQ content). Another example: Article schema with an Author field and publication date. The model can automatically read who the author is (which supports trust if the author is, say, a doctor or lawyer) and when the text was created (which helps assess freshness). In structured data you can also include information about awards (e.g. Award in organization or person schema), reviews (Review), ratings, etc. – all of this builds your E-E-A-T profile that the LLM can pick up. Just remember that schema should be correct and consistent with the content. Errors in JSON-LD can prevent it from being read – it’s worth testing them with Google’s structured data tools. Here’s a simple example of FAQ schema that could appear in the page code:

Such a structure ensures that both Google and any other bot immediately “see” a question and answer on your site. If the user asks a very similar question, there is a good chance your ready-made Q&A fragment will be retrieved and used as an answer (possibly almost word-for-word, because it was provided in an ideal format).

Strengthen trust and expertise signals – in the LLM world you must prove your credibility with your own content. Here’s what helps:

Add visible author information to articles. Ideally by name, with a short bio (“Jan Kowalski, SEO analyst with 10 years of experience…”). You can link the name to an “About the author” page with more details (education, achievements). Models are increasingly good at recognizing authorities – if someone writes frequently in a niche and is always signed, the model can connect the dots and rate them higher than an anonymous copywriter.

Highlight awards, certificates, affiliations. If your company or you have industry distinctions, partnerships with recognized organizations – mention it in the content or in the footer. For example, add a “Our awards” section or badges (“Winner of Best AI Startup 2024”). In traditional on-page SEO this used to have little meaning, but for LLMs it is a signal: “aha, this company has been recognized in the industry, it can be trusted more”.

Encourage reviews and testimonials and display them. On product or service pages, place genuine customer reviews. Models can identify them and treat them as another proof that given claims are socially verified. In classical SEO, review stars in schema could boost CTR, whereas here the actual review content can even be quoted by AI (“User X confirms that…”).

Link to sources and research in your texts. Although it may seem you don’t want to send the user off your site, sourcing important statements (e.g. via report citations or statistics with the institution name) makes your content more trustworthy. AI, seeing a footnote or phrase like “(according to the Gartner 2023 report)”, will assess that the author has made an effort to be reliable. Moreover, the model may recognize well-known institutions in your text and strengthen the association with their authority. Note: this does not mean copying entire fragments from other sources – AI punishes duplicates. It’s about short quotes or simply stating a fact and adding “who says so”.

Maintain consistency of entity information on your site. If your brand is called “XYZ Sp. z o.o.” in one place, “X.Y.Z.” in another, and the product “SuperWidget 3000” vs. “Super Widget”, AI may fail to link these and treat them as different entities. Stick to uniform names and profiles. You can support consistency using schema.org/Organization with fields for official name, aliases, social media profiles (also a trust signal: a link to an active LinkedIn profile, for example).

Use security protocols: HTTPS is an absolute must – not only for SEO but also because Chrome and other browsers (and therefore crawlers) may not index insecure (HTTP) resources. Avoid malware, suspicious scripts, etc. – models may have a “blacklist” of unsafe sites.

Optimize performance and freshness – although AI does not wait for a page to load like an impatient user, a fast, optimized site makes crawling easier (fewer timeouts or time-limited omissions). So focus on:

Removing unnecessary scripts and elements that add no value (every extra MB to download is a potential problem).

Using SSR (Server-Side Rendering) or prerendering for SPAs – so the bot always gets full HTML.

Correct use of HTTP headers (status codes). Each subpage should return 200 OK if it is available. 4xx/5xx errors or redirections can cause a page to be dropped from the AI index. Monitor Crawl Errors in Google Search Console and in logs.

Regularly updating important content and using lastmod in the sitemap to signal changes. If you have an article “State as of 2023”, create a new one for 2024 or update the existing one – models prefer newer information for fresh queries. Freshness is a contextual signal: e.g. when asking today about “latest studies on LLMs”, AI will choose a fragment from 2025 rather than 2020, assuming comparable content quality.

In the context of LLMs, the challenge of language versions and regions also arises: make sure you have implemented hreflang correctly for multilingual sites, because e.g. Bing Chat is multilingual by default and may quote your site in the wrong language version if it doesn’t know which one fits the question. Also check the geolocation of hosting/CDN – extremely long server response time for global bots is a negative.

Consider preparing an llms.txt file – this is a new concept in the AEO world: analogous to robots.txt, the llms.txt file (location: yourdomain.com/llms.txt) is intended not to block, but to guide AI as to which content is most important on the site. The format of this file is being shaped – in practice it is a kind of condensed guide to your knowledge in a model-friendly form (concise text sections, links to key pages, summaries). For example, a library might list in llms.txt:

Documentation

• User Manual – basic introduction to the product.
• Product X FAQ – answers to the most common questions.

Technical data

• API Specification – details for developers.

The idea is that when AI (e.g. an assistant chatbot) receives a question about your product, it can on the fly refer to llms.txt and see: aha, here is a link to the FAQ, here to the specs – it fetches those pages and easily creates a complete answer. llms.txt is, therefore, a kind of mind map of your domain for AI. It does not replace normal indexing (you still need a sitemap and SEO), but it complements it from the AEO side. Implementing llms.txt is currently optional and no mainstream bot officially uses it yet, but the initiative is gaining traction in the industry. It is worth knowing about it, because in the coming years it may become a standard (similarly to how ads.txt became a standard in advertising). If you already have the capacity – prepare such a file manually, link it next to the sitemaps in robots.txt (Allow: /llms.txt). Even if it doesn’t bring results today, you will be one step ahead of the competition when AI starts using it.

Monitor, test, and improve – the last step is less of an implementation and more of an ongoing practice:

Track server logs and crawl stats: check whether your logs show AI bots (e.g. PerplexityBot/1.0 or GPTBot). Analyze which URLs they visit, how often, and whether they receive correct 200 responses. If you see attempts to access URLs they shouldn’t (e.g. strange parameters), maybe something needs updating in the sitemap or some unimportant paths need to be blocked.

Use SEO tools to simulate crawling: Screaming Frog, Sitebulb or the Ahrefs Crawler will let you see your site as a bot does. It’s worth setting them to “text-only” or “JavaScript off” mode to simulate a simple AI bot. You will then see which content is visible without JS, where alts are missing, whether the heading structure is logical. Fix what the audit shows.

Check visibility in new interfaces: If you have access to SGE (Google Search Generative Experience), test queries related to your industry, check whether your pages appear in links suggested by AI. Similarly on Bing – ask Bing Chat about topics you cover. A good practice is also using Perplexity.ai or other knowledge chatbots and asking questions that your site should answer. If they never cite you – that’s a signal something is wrong (maybe the bot doesn’t know your site or considers it low quality).

Use Google Search Console and Bing Webmaster Tools: GSC is starting to expose AI-related data (e.g. in the US, a beta showed clicks from SGE separately). Even if in Poland this is still emerging, watch the “Enhancements” or “Search appearance” area to see if anything AI-related appears. Bing WMT may not report Chat, but remember that IndexNow and other new Bing features can help with faster indexing – use them to quickly deliver content that Bing Chat will later use.

Stay up to date with AEO trends: Follow industry blogs (in Poland and abroad) – new case studies keep appearing, showing how LLM-oriented optimization brings results. Perhaps someone in your niche discovers that a certain content format (say, a comparison table) is often cited by AI – worth implementing such elements yourself.

Real-world perspective: tools and examples

Understanding the theory is one thing, but what does AEO look like in practice? Here are some real aspects and examples:

Crawling and analytics tools: Your old SEO friends are still useful. Screaming Frog SEO Spider can help you generate the entire site structure and list elements that may hinder AI (e.g. pages without alt descriptions, without an H1 heading, with duplicate titles, or low word count – all red flags for AEO). Google Search Console is the bare minimum for monitoring indexing state – make sure you don’t have Discovered – currently not indexed issues for important subpages. If Google doesn’t index a page, Bing Chat or SGE also won’t see it. Bing Webmaster Tools is often overlooked, but in the age of Bing Chat it’s worth looking there – it lets you request reindexing of URLs and check rendering performance.

Using logs and web tools: Raw server logs can be hard to analyze manually, but tools like Splunk, ELK Stack, or dedicated services (e.g. Botify) can extract bot traffic insights. There are already the first GEO dashboards – e.g. Perplexity or the now-defunct Neeva sometimes shared top source lists. Some platforms like Cloudflare plan integrations to show how much traffic AI bots generate. Be prepared for a new type of analytics report: not only SEO traffic, but also AI-citation traffic or AI impressions.

Case studies in the industry: Around the world, reports are emerging showing how large publishers feel AI’s impact. For example, news portals have seen a drop in search traffic among younger users, as they prefer asking AI for news. Those that were cited in responses (e.g. Reuters, Wikipedia) still gained trust and indirect traffic. In e-commerce, companies invest in their own chatbots powered by their own indexes – which proves the effectiveness of the approach described here (if they build their own vector databases with FAQ and documentation content, then global AI likely do the same at greater scale).

New success metrics: Traditionally, we looked at rankings and CTR. In AEO, we must think in terms of AI citation rate. It is hard to measure directly, but we can approximatively check: does our content appear in answers (as far as we can see)? Some companies test dozens of queries in ChatGPT or Bing and manually note who AI cites. If it’s always your competitor and never you – that’s a sign you need to improve content for chunking, E-E-A-T, etc. Perhaps we will see official tools – Google is experimenting with showing in GSC “Your site appeared X times in AI Overview”. Once that happens, an SEO specialist will have to factor this metric in alongside impressions and clicks.

Knowledge platforms and partnerships: Realistically, not everything can be solved by on-site SEO. If you want to be highly visible in the AI era, think about where else AI draws knowledge from in your domain. For example: if you run an electronics store, AI’s answers to questions about specs may come straight from the manufacturer’s structured data database or from Wikipedia (which meticulously lists parameters). To have your brand appear in such answers, you can provide unique tests not found in official specs, or collaborate with industry portals (guest expert articles). AI likes to combine sources – if your e-commerce site is not yet an authority, it may be worth being cited on a site that is, so the model registers your presence.

Impact on SEO, AEO and LLM visibility

Adapting to the above guidelines has tangible effects:

Better brand visibility in AI answers: Your content becomes part of the answer, so even without clicks, the user learns about your brand as a knowledge source. It’s somewhat like being quoted as an expert in the press – it builds reputation. In the longer term, this can lead to “brand search” – users start to associate that “on blog X there are great analyses”, so they search for your brand directly or trust answers where you are the source.

Indirect traffic from AI: Although many AI answers are consumed without a click, some users do click the source to learn more. Especially when the answer is brief or sparks curiosity. By being cited you can still gain valuable traffic – perhaps lower in volume than before from SERPs, but more engaged (if someone clicked despite already having an answer, they are truly interested).

Synergy with traditional SEO: Importantly, LLM-focused optimizations mostly do not conflict with SEO – in fact, they strengthen it. Improving page structure, speed, adding schema, better content, internal linking – all are also ranking factors in Google. Thus, by doing AEO you simultaneously benefit in classic SEO. As a result, your site may dominate twice: both as a link in Google’s top 10, and as a quoted fragment in AI.

Changes in keyword strategy: In the AI era, optimizing for exact phrases becomes less important (because the LLM understands synonyms anyway). Topic and intent coverage matters more. It may turn out that content focused on satisfying the user (comprehensive, well-organized) performs excellently in LLMs, while old tricks like “keyword stuffing” do absolutely nothing. The impact is that SEO must evolve – content strategy should be built around key topics and user questions, not only around exact phrases. Tools like AnswerThePublic or People Also Ask become more important than ever – because questions are the new unit of the visibility battle.

Zero-click and new KPIs: As mentioned, be prepared for reports where organic traffic may drop, but that does not necessarily mean you’re doing worse – it may simply be that people get answers without clicking. You will have to measure, for example, brand mentions, brand traffic, conversions assisted by the AI channel (e.g. someone first read you as a quote, and later came to the site and made a purchase). AI’s impact on the customer journey may be non-obvious – perhaps AI will reduce top-of-funnel research (by immediately giving certain suggestions), and more queries will be transactional right away.

New competition: Remember, an LLM can combine information from many sites into a single statement. This can work in your favor (if you are one of the few sources on a topic, AI will use you), but it can also flatten differences between sites. If your content adds nothing unique and someone else’s has the same, AI may randomly or rotationally cite you or your competitor. That’s why differentiation is so important: your own data, a unique experiment, a custom infographic described in text – something that cannot be found elsewhere. Then AI has no choice, it must use you, because only you have this gem of information.

Impact on link building and marketing: Finally, this may change how we think about link building and PR. Since links for ranking have somewhat lost importance (at least in the context of AI answers), it’s more about having other sites talk about you, not only link to you. A big quote, mention in a popular industry report, presence in statistical datasets – all of this can make AI notice you. Content marketing will thus be oriented toward “being cited/reshared” not only by people, but also by AI algorithms.

Typical mistakes and difficult cases

When implementing LLM optimization, it is easy to stumble. Here is a list of the most common errors and tricky situations sites encounter:

Excessive reliance on JavaScript: SPA (Single Page Application) sites or very dynamic sites can look great to users, but if you do not implement prerendering, they will be empty for many AI bots. The mistake is assuming “since Google renders this, others probably do too”. Not true – many AI crawlers do not have resources to run full JS. The solution: implement SSR or serve a static version for bots (just beware of cloaking – the content must be the same as for users).

Blocking the wrong things in robots.txt: Sometimes developers accidentally block something crucial (e.g. the entire /images/ or CSS files needed for rendering). This makes correct page interpretation harder. Blocking JSON-LD files (sometimes kept in /scripts/) is especially bad. Make sure no resources needed to understand the page are blocked. If you fear duplication (e.g. the site is generated in two versions and you want to block one), use meta-robots noindex rather than a global Disallow – Disallow means the bot will not see anything in this section, not even that noindex is there.

Lack of canonicalization and duplicates: While indexing, an LLM may encounter many URLs with the same content (e.g. parameter versions, session IDs, filtering, etc.). If you don’t mark a canonical page, it may accidentally index some “impaired” version (e.g. without category context). This is analogous to SEO – always specify where necessary. This also applies to mobile versions (if you have a separate m.), or pagination (provide rel prev/next). AI generally tries not to get lost, but the cleaner, the better.

Chunks too big due to poor formatting: If you write very long paragraphs, e.g. 20–30 sentences in one block, the chunking algorithm may leave them as one (it didn’t find a place to split). Such a chunk may be too large for the model to want to use in full and may be discarded. It’s better to divide thoughts into shorter paragraphs (3–5 sentences). This not only makes reading easier for humans, but also for AI. The mistake is a “wall of text” – in SEO it used to be semi-acceptable, in AI it is almost a guarantee that the wall will remain untouched.

Writing for SEO, not for people: Paradoxically, AI enforces more human writing. If someone still generates mass texts saturated with keywords without coherence, the generative model detects this as low-value content (we have seen cases where “bland AI-styled paragraphs with no specificity” are ignored). A common mistake – using ChatGPT to write articles “as a shortcut” and posting them on the site hoping it will help with AI. Unfortunately, if you could generate it, it means the model that generated it has already seen something like this thousands of times. Your embeddings will not be unique. As a result, your content will be transparent to another LLM – it will not stand out from hundreds of similar sentences. The remedy: add a layer of your own knowledge, uniqueness. AI-SEO is not about chasing the algorithm, but paradoxically returning to expert content.

Ignoring metadata and technical details: Some say: “since AI only looks at content, the Title tag or meta description doesn’t matter”. Indeed, the Title and meta description are no longer what will be displayed to the user (the user gets a conversational answer). But that does not mean they are meaningless! The Title can still be used as context – e.g. in the vector index the system records “fragment X, and the page title is …”. This helps in ranking/filtering. Similarly, a well-written meta description may serve as the snippet under the link in AI Overview. The mistake is neglecting old good practices: a Title with a unique, relevant title; meta description with a summary; using meta author, meta date if you don’t have schema (something still signals to the bot who and when). Similarly sitemap.xml: seemingly oldschool, but as we have seen – Perplexity appreciates the presence of an up-to-date sitemap. Do not dismiss too quickly things that “were only for SEO”.

Problems on multilingual and localized sites: An edge case is when your site exists in many languages or in versions per country. AI may not always choose the right one – for example, someone asks in English and your English content is weak, while the Polish version is comprehensive – the model may even use the Polish fragment and translate it (there were cases where Bing quoted a site in another language and translated it on the fly if nothing was found in the target language). To avoid such situations, make sure that each language version is equally polished and correctly connected via hreflang. If you don’t plan to translate e.g. the blog into every language, perhaps it’s better to exclude those pages from indexing in languages you don’t support (so they don’t introduce chaos).

Paid / login-only content: If your business model is based on a paywall, you have to accept the fact that AI will rather not take into account content behind it (unless we’re talking about special integrations, such as Bing’s with the NYTimes for subscribers). For an AI crawler, such a page looks empty or with a summary. It would be a mistake to think you can circumvent this by e.g. serving full content to the bot (that would be cloaking, which Google may punish). There is no perfect solution for now. You can possibly provide part of the content for free (e.g. extensive fragments or PDF reports), so that at least some of your expertise ends up in the AI index, leaving the rest paid. It’s a dilemma: you want to be cited (content must be visible) vs. you want to sell subscriptions (content hidden). Observe traffic – perhaps traffic from AI citations is valuable and converts, and then you might reconsider your business model.

Not following ethical guidelines: AI generates answers, but the companies behind it have policies – e.g. they cannot quote sites spreading disinformation, hate, fraud. If your content unintentionally falls into these categories (e.g. no disclaimer on speculative medical content), it may be skipped. Therefore, it is worth self-moderating in line with E-E-A-T: provide sources for controversial theses, avoid categorical advice where a specialist should decide (unless you are one and clearly state it). AI will trust a site that appears neutral and professional more readily.

Summary – key actions

Finally, let’s collect the most important, practical steps you can take right away to prepare for the LLM indexing era:

Open your site to AI: allow new bots (GPTBot, PerplexityBot, etc.) to crawl and remove technical barriers (logins, IP blocks). Monitor their visits.

Structure and chunk your content: redesign content so it is split into small, self-contained fragments (headings, paragraphs, lists, FAQs). Each fragment should answer one question or cover one topic.

Use structured data and metadata: add schema.org (Article, FAQ, HowTo, Product – whatever fits) and make sure each page has a meta title and description. Mark the author, FAQ sections, ratings – anything that gives algorithms extra context and confidence about quality.

Strengthen content credibility: weave trust elements into your content – an author with expertise, mentions of awards, sources of information, up-to-date data. Let your site itself demonstrate it is expert and worth quoting.

Remove “black magic” SEO: get rid of practices like keyword stuffing at the expense of natural language. Focus on user value – AI evaluates text similarly to an intelligent reader, ignoring trivial marketing fluff.

Ensure technical cleanliness: make sure key content is available in HTML immediately (not hidden behind clicks or scripts). Optimize speed, fix 404/500 errors, fill in hreflangs, update the sitemap. Break large text blocks into smaller ones.

Test on a living organism: ask ChatGPT, Bing, Bard (in the past), Perplexity about topics from your site. Check if and what they quote. This is a practical test of your AEO effectiveness – the result is served on a plate.

Be ready for evolution: AI search will change – follow new developments (e.g. llms.txt, new Google guidelines, new bots). Adapt strategy, treat this as a continuous process, just like traditional SEO has never been “do once and forget”.

By going through the above stages and recommendations, you prepare your site for a future where “being found” means being understood by AI. The anatomy of crawling in the era of artificial intelligence may seem complex, but fundamentally it boils down to the same as always: delivering great content in a clear and user-friendly way (whether the user is a human or a machine). Then, regardless of whether the user reads the article on your site or a summary generated by a chatbot – it will be your information that shapes the answer. And that is precisely the point of AEO.