# What Is LLM SEO? How to Get Cited Beyond Your Own Site Most AI citations point to sites you don't own. Here's how LLM SEO covers both your pages and the sources AI engines rely on. **Published:** February 10, 2026 **Author:** Kate Starr & Connor Lahey --- If your LLM SEO strategy focuses only on your own website, you're optimizing for a source that accounts for just 4% of citations in category questions. The rest come from other websites, including your competitors'. So, optimizing just your own pages isn't enough for LLM SEO. You also need to understand which external sources AI engines rely on and how your brand shows up across them. In this article, you'll learn what to optimize on your own site, where else AI engines get their information from, and how to measure your AI visibility. ## What is LLM SEO? LLM SEO is the process of improving your visibility in answers from AI tools like ChatGPT, Perplexity, Gemini, and Google AI Mode. LLM stands for large language model, which is the technology these tools use to understand questions and generate answers. You might also hear this called AI SEO, [AEO](https://www.searchable.com/blog/what-is-aeo), and GEO. These terms describe similar approaches to improving visibility in AI search. Traditional SEO focuses largely on whether your pages rank in search results. With LLM SEO, you're also looking at whether AI engines find and use your content as a source, cite your pages, or mention your brand in their answers. While rankings and organic traffic still matter, they don't give you the full picture of how visible your brand is in AI-generated answers. LLM SEO includes many of the same foundations as SEO, such as useful content, clear structure, reliable evidence, and technically accessible pages. However, optimizing the content on your own website only covers one part of where AI engines get their information. ## Where AI answers actually get their sources AI answers get their sources from a mix of brand websites, competitor websites, editorial publications, third-party sites, institutional sources, social platforms, review sites, and forums. We analyzed more than 30 million citations from unbranded category prompts over six weeks, across roughly 8,700 tracked brands, to understand how that mix breaks down: ![Bar chart of the share of AI citations by source type in answers to unbranded category prompts: a competitor's own site 41.7%, editorial 19.8%, third party 12.8%, other 9.8%, institutional 5.9%, the brand's own site 4.2% (highlighted), social 3.1%, review 1.6%, and forum 1.2%](https://www.searchable.com/blog/llm-seo-citation-sources.webp) "Other" covers sites that don't fit the remaining categories, such as marketplaces like Amazon and Etsy. When someone asks a category question that doesn't name your brand, your own website accounts for just 4.2% of the citations. For a typical brand on ChatGPT and Perplexity, its own website is cited in only about one in 20 of the category answers that cite any source. The competitor share combines citations to all competitors tracked for a brand, while the 4.2% share covers only that brand's own website. That means the 41.7% and 4.2% figures aren't directly comparable. These findings apply specifically to unbranded category questions. When someone asks about a company by name, AI engines can rely on a very different mix of sources. Over the same six weeks, a brand's own website accounted for 41.2% of citations when the prompt named the brand, compared with 4.2% when it didn't. Around 54% of citations point to sites that don't belong to the brand or its competitors (more on that later). You can't control those pages, but what they say can still shape your AI visibility. These sources include publishers, review sites, forums, and other third-party pages. For LLM SEO, this whole analysis means your own website is only one part of the source pool you need to work on. ## How LLMs choose what to cite LLMs use a process called Retrieval Augmented Generation (RAG) to find and cite sources. They search the web for relevant content, judge it on authority, freshness, and clarity, then build an answer citing the best sources. Think of it like a research analyst briefing a CEO. The analyst doesn't write from memory. They pull the most relevant, recent, and credible papers from a library. They read them, pick out the key findings, and write a summary with sources. The CEO gets only the curated brief, instead of reading the full library. RAG works in three steps: 1. **Retrieval.** The LLM looks for information related to the user's prompt. A single question can trigger several related searches through [query fan-out](https://www.searchable.com/blog/what-is-query-fanout-aeo-glossary), which gives the engine a wider pool of potential sources. Traditional SEO signals like domain authority, indexing, and backlinks shape what the LLM can see at this stage. 2. **Evaluation.** The engine decides which retrieved sources and passages are useful enough to include. Relevance, source quality, content freshness, and how clearly the page answers the question can all affect what it selects. 3. **Synthesis.** The LLM combines information from the selected sources into one response and may cite the pages behind specific claims. One answer can therefore pull from several websites rather than relying on a single page. If hundreds of sources describe your brand the same way, the model has more consistent information to draw from when generating an answer. **What to do about this information:** Strengthen your SEO to make sure LLMs can find your page during retrieval. Keep your content accurate, current, and well-supported so it has a better chance of passing the evaluation stage. Then structure important information clearly and strengthen your brand authority so your brand and content are easier to use in the final answer. ## The part you control: on-site work that still matters On-site LLM SEO is what you can control directly, including your content structure, proof points, how current the content is, and whether AI engines can access it. ### Structure content so it can be extracted AI engines often work with individual passages. So, each section needs to make sense on its own. Use a clear H1, H2, and H3 hierarchy without skipping levels and answer each heading directly in the opening paragraph. For longer articles, add extractable elements like tables, lists, or specific statistics throughout the page. These give AI engines clearly defined pieces of information to retrieve. This doesn't mean turning every article into a collection of short answer boxes, though. Your content should still read naturally from beginning to end. ### Publish original data and named expertise Give AI engines information they can't get from generic summaries or other pages. First-party research, real examples, named subject matter expert input, and properly sourced statistics all add information that generic summaries can't. In a [2024 study](https://collaborate.princeton.edu/en/publications/geo-generative-engine-optimization/) on generative engine optimization (GEO), researchers from Princeton, IIT Delhi, and other institutions found that adding statistics, quotations, and source citations improved visibility in their benchmark by up to 40%. Adding one statistic doesn't guarantee a 40% lift, but it does support the broader case for publishing specific, attributable evidence instead of unsupported claims. ### Keep important pages fresh Update your pages when the information on them changes, especially for topics like software features, pricing, regulation, benchmarks, and industry research. Outdated details make a page less useful even if the rest of the content is still accurate. Replace old statistics, remove examples that are no longer relevant, update product information, and add newer evidence where it improves the answer. ### Strengthen your technical foundations To make sure AI engines can access your pages, keep important ones crawlable and indexable. Put key information in the rendered HTML, use clear internal links, and fix technical issues that make pages harder to crawl, access, or understand. Structured data can also help search engines understand what a page contains and who created it. Use relevant schema, such as Article markup with author and dateModified details, where it accurately reflects the content. Use FAQPage markup when the page contains corresponding FAQ content. Schema is also worth implementing as part of good technical SEO. However, there isn't enough evidence to say that schema markup increases AI citations on its own. ## The larger part: earning citations on third-party pages To understand which third-party sources have the widest reach, we ranked the top cited domains by the number of tracked brands they affect, rather than by total citation volume: | Domain | Type | Brands affected | | :---- | :---- | :---- | | reddit.com | forum | **4,814** | | youtube.com | social | 4,778 | | facebook.com | social | 3,716 | | linkedin.com | social | 3,543 | | wikipedia.org | institutional | 2,858 | | instagram.com | social | 2,410 | | forbes.com | editorial | 2,249 | | quora.com | forum | 2,031 | | trustpilot.com | review | 1,413 | | g2.com | review | 1,320 | Reach is more useful than raw citation volume here. For example, nih.gov received more than five times as many citations as Wikipedia (209,349 vs. 38,844) but affected fewer brands (1,950 vs. 2,858). Ranking by brands affected shows which domains are relevant across the widest range of companies. With that in mind, focus on building visibility on the third-party domains that matter in your category: ### Build your presence on forums Reddit appeared in citations for 4,814 of the roughly 8,700 brands we analyzed, making it one of the two outside domains with the widest reach in this dataset, alongside YouTube. On ChatGPT alone, that number drops to 882 brands. Quora is the next-biggest forum in our data, appearing in citations for 2,031 brands. Nearly 90% of those citations came from Google AI Overviews, so Quora is worth your time if Google's AI results matter in your category. Forums give AI engines access to discussions based on real questions, recommendations, and experiences. The best way to show up is to participate in discussions where your expertise is useful and relevant to the question. ### Keep your video and social profiles active Social platforms take four of the top six spots in our reach table. YouTube appeared in citations for 4,778 brands, almost as many as Reddit. Facebook, LinkedIn, and Instagram each reached more than 2,000 brands. Social pages make up only 3.1% of citations, but they show up for a wide range of brands. Most of that reach comes from Google AI Overviews. For example, AI Overviews cited YouTube for 4,302 brands. LinkedIn is the exception, because Perplexity cites it for more brands than any other engine does. So, keep your company profiles complete and accurate, and publish videos and posts that answer the questions buyers ask in your category. ### List your business on relevant review and comparison platforms Review platforms like G2, Clutch, and Trustpilot also appear across more than 1,000 tracked brands each. These sites organize information around the kinds of comparison and evaluation questions buyers often ask, including alternatives, ratings, and customer experiences. Start with the platforms buyers in your category already use. Keep your company information accurate, encourage customers to leave reviews, and respond to feedback where appropriate. ### Earn editorial coverage and digital PR Editorial sites can become another source AI engines use when answering questions about a market or category. Forbes alone appeared in citations affecting 2,249 brands in our dataset. You can't edit these pages yourself, so the goal is to earn useful [third-party coverage](https://www.searchable.com/blog/aeo-strategy-earned-owned-decision-tree). For example, original research and newsworthy insights from your company can give journalists both a reason to cover your brand and a useful source to reference. Digital PR helps you put those assets in front of the right publications and journalists. Focus on building credible coverage that accurately describes your brand, expertise, and category. ### Build accurate institutional and reference mentions Institutional and reference sources also have substantial reach. Wikipedia, for example, appeared in citations affecting 2,858 tracked brands. These sources tend to have stricter inclusion rules, so you can't approach them like ordinary marketing channels. Instead, focus on making factual information about your company easy to verify through reliable sources, consistent company details, and credible third-party coverage. While you can't always control what an institutional source says about you, you can improve the quality of the evidence available for it to draw from. ## Does AI traffic actually convert? AI-referred visitors can be valuable because they often arrive later in the buying process. They may have already used an AI engine to compare options, narrow down their choices, and answer questions before clicking through. That makes the value of each visit more useful to track than traffic volume alone. Previsible analyzed [1.96 million LLM-driven sessions](https://previsible.com/seo-strategy/ai-seo-study-2025/) and found that AI accounted for just 0.13% of total sessions. However, that traffic was concentrated on decision-focused pages, including pricing, tools, and industry pages, where visitors are more likely to be evaluating options rather than casually browsing. At the same time, AI referrals are growing quickly. Similarweb found that AI platforms sent an average of [770.7 million referral visits a month](https://aisearch.similarweb.com/blog/ai-referral-traffic-by-industry/) between June 2025 and May 2026, up 117% from the year before. For now, AI traffic is still a small but growing channel. Track how much traffic it sends, which pages those visitors land on, and what they do next. This gives you a clearer picture of its value than assuming AI-referred visits will convert by default. ## How ChatGPT, Perplexity, and Google AI Mode differ Each AI search platform finds and cites sources in its own way. ChatGPT draws on its [training data](https://www.searchable.com/blog/where-does-chatgpt-get-its-data) plus live results from OpenAI's own crawler and third-party search providers, including Bing. Perplexity runs its own search index and fetches pages live when it answers. Google AI Mode uses Google's own index, and pages that rank higher are more likely to be cited. | Feature | ChatGPT | Perplexity | Google AI Mode | | :---- | :---- | :---- | :---- | | **Search index** | OpenAI crawler + third-party search, including Bing | Own index + live page fetching | Google | | **Citation style** | Inline links + source list | Numbered citations | Link cards beside the answer | | **Community sources** | Low | High | High | | **What to focus on** | Search visibility + authoritative third-party sources | Fresh content + strong citations | Traditional Google SEO | ### ChatGPT ChatGPT finds pages through OpenAI's own crawler and third-party search providers, including Bing. It sent [more than 80% of AI referrals](https://aisearch.similarweb.com/blog/ai-referral-traffic-by-industry/) to the top 1,000 websites worldwide in 2025, making it the single most important platform for AI visibility. ChatGPT also cites Reddit far less than Perplexity and Google's AI engines do. In our data, Reddit appeared in ChatGPT citations for only 882 brands, compared with 4,814 across all engines. Instead, more than half of ChatGPT's citations point to brand and competitor websites, and it leans on reference sources like Wikipedia. So, keep your pages crawlable, keep content fresh, and build a presence on the authoritative sites in your category. If you can only pick one AI system to optimize for, start with ChatGPT because it sends the most AI traffic by far. ### Perplexity Perplexity runs its own search index and fetches pages live when it answers. Its real-time crawling means newly published or updated content gets discovered fast. Perplexity also still cites Reddit widely. In our data, Reddit appeared in Perplexity citations for nearly two-thirds of the brands in our Perplexity data. To perform well in Perplexity, lean into original research and strong citations. ### Google AI Mode [Google AI Mode](https://www.searchable.com/blog/google-ai-mode) only cites pages that are [indexed and eligible](https://developers.google.com/search/docs/appearance/ai-features) to appear in Google Search. Ranking higher raises your chances, but AI Mode also runs related searches through query fan-out, so it can cite pages that don't rank in the top 10 for the original question. If a page isn't indexed, it can't appear in AI Mode, so make sure to optimize for traditional search to get cited in Google AI Mode. ## How to measure your LLM visibility LLM visibility measurement is still less straightforward than traditional rank tracking because AI engines can give different answers to the same prompt over time. The most useful approach is to track a consistent set of category prompts and watch what changes. When you [track AI visibility](https://www.searchable.com/blog/ai-visibility-tracking), focus on three things: - **Where your brand appears against competitors.** Track which prompts mention or cite your brand, which competitors appear when you don't, and how often that happens across different LLMs. - **Which sources shape AI answers in your category.** Track which publications, review sites, forums, and other domains AI engines cite most often. This shows where you may need a stronger third-party presence. - **How those patterns change over time.** Track the same prompts regularly to see whether your brand is showing up more often and which competitors are gaining citations. You can also monitor [AI referral traffic](https://www.searchable.com/features/traffic) in GA4 to see which engines send visitors to your site and which pages they land on. GA4 only captures visibility that results in a click, so it won't show your full visibility inside AI answers. [Searchable](https://www.searchable.com/) automates this process by reliably tracking prompts across nine AI engines. It compares your presence with competitors, and its [source analytics](https://www.searchable.com/features/aeo-insights/sources) show which sites those answers cite: ![Searchable All URLs table listing the pages AI answers cited for a bike brand, with the domain, source type, and citation count for each, and the editorial, forum, and social rows from bikeradar.com, reddit.com, youtube.com, and rei.com boxed](https://www.searchable.com/blog/llm-seo-sources.webp) ## Frequently asked questions ## Ready to build your LLM SEO strategy? Before deciding what to optimize, you need to know how visible your brand is right now. Get your [free AI visibility report](https://www.searchable.com/free-visibility-report) to see where you already appear, which competitors are showing up with or instead of you, and what to improve. From there, you can come back to this guide to build your LLM SEO strategy around what actually needs attention first. --- [Back to Blog](https://www.searchable.com/blog) | [Searchable Homepage](https://www.searchable.com)