LLM Optimization (GEO): Optimize for AI Overviews & ChatGPT | Via Marketing
LLM Optimization: How to Optimize Content for LLMs and AI Overviews (GEO)
Optimize content for LLMs (GEO): answer-first structure, schema, citations, internal links, and freshness for AI Overviews, ChatGPT & Gemini
39 min read
Share:
Large Language Models (LLMs) like OpenAI’s ChatGPT, Google’s Gemini (via Google Search’s AI Overviews and AI Mode), Anthropic’s Claude, xAI’s Grok, and Perplexity are increasingly acting as intermediaries between users and web content. To ensure your website’s content is AI-friendly and optimized for LLMs , focus on both on-page elements (structure, clarity, data markup) and off-page factors (authority, freshness, external signals). Framed as LLM optimization within AI SEO and GEO , the goal is to create content that RAG systems can confidently ground, retrieve, and quote.
Below, we detail:
2. Key elements that make a webpage easy for LLMs to read, interpret, and summarize (technical and non-technical, internal and some external aspects).
4. Known or inferred parameters that influence how LLM-based systems select and cite pages when generating answers, essentially, factors affecting which pages LLMs deem worthy to pull information from.
We’ll reference the mechanisms that make this work, Retrieval-Augmented Generation (RAG) , NLP , embeddings , vector databases , and the Knowledge Graph and surface advanced signals (e.g., llms.txt ) that improve crawlability , semantic search alignment, factual grounding , and the use of verifiable anchor points .
TL;DR:
LLM optimization (also called Generative Engine Optimization or GEO ) is the practice of structuring web content so AI systems (like ChatGPT , Google AI Overviews , Claude , Gemini , and Perplexity ) can understand, retrieve , and cite it. Prioritize answer-first content , E-E-A-T-driven semantic richness , and Schema.org structured data so RAG systems can lift accurate, verifiable snippets.
Quick LLM content optimization checklist
If you’re looking for a prioritized checklist for LLM content optimization , start here:
- Lead with an answer-first TL;DR (then expand with detail below).
- Use descriptive H2/H3s , short paragraphs, and lists/tables so the content format improves LLM extractability .
- Reduce ambiguity: define entities, avoid unclear pronouns, and make each section self-contained .
- Add citations, quotes, and statistics (with context) as verifiable anchor points.
- Implement Schema.org (Article/BlogPosting, FAQPage, HowTo, Product, Organization, etc.) and include dateModified .
- Strengthen internal links so LLMs can understand topic relationships across your site.
- Keep pages fast, crawlable, and accessible (text available in HTML; alt text for images).
- Verify you’re not unintentionally blocking AI crawlers you want to allow (training vs search vs user-initiated retrieval).
- Update time-sensitive facts and show a visible Last updated date.
- Test queries in ChatGPT/Claude/Perplexity and in Google Search (AI Overviews) to see what gets cited.
1. What is LLM-friendly content? How to optimize content for LLMs and AI
LLMs “read” web content similarly to humans , favoring clear organization, concise language, and well-structured data over keyword-stuffing or clutter. If you’re asking “what is LLM-friendly content?” , it’s content that’s answer-first, structured for easy snippet extraction, and marked up for machine readability so models can cite it correctly. An LLM-optimized page helps the model quickly grasp your content and retrieve accurate snippets. Key page elements include:
LLM optimization vs. general AI extractability (what to optimize for)
You don’t need two separate strategies:
- LLM optimization is largely about making your content easy to retrieve and cite in LLM-driven answers (ChatGPT, Claude, Perplexity, Google AI Overviews/AI Mode).
- General AI extractability is the same core idea, but broader: ensuring any machine system can parse and reuse your content accurately.
In practice, the overlap is huge: if your content is clear, structured, verifiable, and crawlable, you improve both classic SEO and AI visibility. Where LLM optimization goes further is a heavier emphasis on answer extractability , entity clarity , and citable evidence .
Clear Structure and Formatting (Answer-First Content)
Well-structured content is easier for LLMs to parse and extract answers from. This is the backbone of GEO content structure and formatting . Use descriptive headings (H2, H3, etc.) to organize topics logically, keep paragraphs brief, and leverage lists or tables for structured information. Clean formatting acts as a “signal of clarity” for both AI and human readers . For example, a page that is divided into clear sections with headings, bullet points for key facts, and a logical flow allows an LLM to identify relevant chunks confidently. In fact, studies show that scannable pages (with headings, lists, and short blocks of text) score much higher in usability and by extension are parsed more accurately by AI. A few best practices for formatting include:
- Use a hierarchy of headings and subheadings to delineate topics (H1 for title, H2/H3 for subtopics).
- Keep paragraphs short (2-3 sentences) and focused. Long walls of text can confuse models (just as they do readers).
- Utilize bullet points or numbered lists for steps, facts, or enumerations. LLMs can more easily digest list items than dense paragraphs.
- Include tables for comparisons or data where appropriate. Structured tables can be mined for exact values or relations by AI.
Clear structure improves machine readability and “chunking” of information. LLMs favor content they can scan and extract without confusion , which boosts the chance of your page being included or quoted in an AI-generated answer. For example, Semrush’s AI Search study found that when ChatGPT Search cites webpages, those pages rank outside the top 20 in Google for the related query almost 90% of the time, suggesting structure + relevance can outweigh traditional rank for LLM citations.
Concise, Plain Language (Clarity of Text)
LLMs have been trained on a wide range of text and respond well to content written in natural, conversational language . Pages that avoid jargon, overly complex sentences, or fluffy filler are easier for AI to interpret correctly. Use a straightforward writing style with proper grammar and clear definitions of terms or acronyms. Content that “sounds human” and informative will be rewarded. Models prefer clear explanations and natural phrasing over keyword-stuffed or robotic text.
This clarity also helps semantic retrieval : RAG pipelines turn passages into embeddings stored in vector databases and match them to user intent using NLP and signals from the Knowledge Graph . Clean, unambiguous phrasing raises the odds that your paragraph is retrieved and cited accurately.
- Use an easy-to-understand tone: Write as if explaining to an intelligent layperson. Avoid needless jargon or, if technical terms are needed, define them clearly. Excessive jargon can lead to misunderstanding or misclassification by the model.
- Be concise and direct: Aim to answer questions or make points in as few words as clarity allows. This not only benefits human readers but also ensures AI doesn’t “miss” the answer buried in verbosity.
- Reduce ambiguity: Avoid unclear pronouns (“it”, “they”, “this”) when the referent could be misread; repeat the entity name when needed. Define acronyms on first use.
- Use synonyms and related terms to provide context (semantic richness) rather than repeating the same keyword. LLMs understand meaning , not just exact-match keywords. For example, an article that naturally incorporates terms like “jogging sneakers” alongside “running shoes” signals to an LLM that it covers the topic broadly, improving relevance.
LLMs perform semantic analysis, they grasp context and intent, not just keywords . Clear, well-phrased content reduces the chance of the model misinterpreting your text. Moreover, if the AI is selecting a snippet to quote, a self-contained, plainly-worded sentence is more likely to be extracted accurately. Conversational yet informative writing increases the odds of being selected as an authoritative excerpt.
Direct Answers and Summaries (TL;DR and FAQs)
Because LLM-based search tools often generate concise answers, it helps to anticipate user questions and answer them directly on the page . Two effective techniques are:
- Provide a TL;DR or summary at the top: A concise “Too Long; Didn’t Read” summary (one or two sentences, <50 words) at the very top of your content can guide AI models to the key answer. This acts like an in-page featured snippet.
- Include an FAQ section: A set of Frequently Asked Questions (with 6-10 Q&A pairs) toward the end of an article reinforces key points and covers query variations. Each question should be phrased naturally (as a user might ask it) and answered briefly and factually.
Examples of high-value FAQ questions to include verbatim for LLM matching:
- How to optimize a website for ChatGPT
- How to optimize content for Google AI Overviews
- How to make my website show up in LLM answers
- Why do LLMs need structured data
- How to use FAQs for LLM optimization
LLMs scan for concise, answer-bearing text to include in responses. By front-loading a summary and explicitly answering likely questions, you make the model’s job easier. In essence, if you don’t provide a quick answer, the AI might grab it from someone else .
Semantic Enrichment and Depth (Semantic Richness)
Beyond just clear writing, LLM-oriented content should demonstrate depth and breadth on the topic , the cornerstone of LLM optimization. Models appreciate when a page covers a concept comprehensively (showing expertise) and semantically (using related terms and examples). This means:
- Cover topics in depth: Long-form, well-researched content (think ~1,500-2,500 words) tends to perform better for LLM visibility than thin posts. Depth signals expertise and increases the chance that some portion of your page matches a user’s precise question. For example, a comprehensive guide with multiple sections can answer various sub-questions, any of which might be what an AI user asked.
- Use semantic and contextual keywords: Incorporate synonyms, related concepts, and examples. For instance, if writing about customer engagement, mention related ideas like retention, loyalty, lifetime value, etc. This semantic richness tells the AI that your content has a broad understanding of the topic, making it more reliable and relevant. Semantic diversity helps LLMs because they recognize different phrasings as connected ideas (e.g., “CRM for small teams” and “customer management for startups” are understood as related).
- Include data, quotes, and references: Tangible facts and figures (with citations) embedded in your content both build human trust and serve as verifiable anchor points for AI. For example, the “GEO” (Generative Engine Optimization) research paper (Aggarwal et al., 2024) reported that adding quotations, statistics, and citations can materially improve visibility in generative AI results compared to similar content without these elements. In other words, factual precision and supporting evidence can set your content apart as an authoritative source that an LLM would prefer to cite. If you have original data or case studies, highlight them (and consider providing them in a structured format, like a CSV download or chart, which advanced models could parse).
Modern LLMs use contextual understanding to judge relevance. Content that thoroughly answers a topic (covering subtopics and related terms) will align better with complex or specific queries, improving its chances of selection. Additionally, factual depth and semantic richness feed the model more signals of credibility. LLM-based systems can cross-check facts across sources; pages that provide concrete, cross-verifiable info (like statistics or expert quotes) are treated as more trustworthy. In sum, depth + breadth = authority in the eyes of an AI. An LLM is more likely to trust and use a page that reads like a definitive reference on the topic rather than a superficial overview.
Structured Data and Metadata (Structured data for LLMs & Schema for AI optimization)
In addition to a human-readable structure, embedding machine-readable metadata helps LLMs and search engines accurately interpret and classify your page content . Implementing Schema.org structured data is highly recommended as part of “LLM SEO” and GEO. Key tactics include:
- Use schema markup (JSON-LD or HTML microdata): Apply relevant Schema.org types like Article, BlogPosting, HowTo, FAQPage, Product, etc. to your pages. This provides explicit context about the content. For example, marking up an FAQ list with FAQPage schema signals to AI-driven systems that your page contains question-answer pairs (which they love for Q&A queries). Similarly, the How To schema can delineate step-by-step instructions. Note: Google has reduced the visibility of FAQ rich results and deprecated HowTo rich results in the SERP for most sites. Even so, the underlying structure (clear Q&A, clear steps) and the structured data can still help disambiguation and machine parsing .
- Leverage metadata for authority and recency: Include an explicit last-updated date on your page (visible to readers) and include datePublished and dateModified in your structured data. This helps both users and machines recognize the page as maintained and recent.
- List your most important pages in priority order (one URL per line or in a short curated list).
- Use short annotations describing what each resource contains.
- Prefer canonical URLs (avoid parameterized or duplicate paths).
- Keep it updated when the site structure changes.
- Consider an llms.txt file: As a newer practice, some sites are adopting an llms.txt (Large Language Models instructions file) at their root, analogous to robots.txt. In it, you can point AI systems to your most important canonical resources (docs, APIs, datasets, policies, key guides) so retrieval systems land on the right pages. A practical way to use llms.txt (including via an “llms.txt generator”) is to:
Structured data gives LLMs confidence in understanding your page. By explicitly telling the AI what each part of your content is , you reduce ambiguity. A well-marked page is more likely to be selected because the model can be sure of what it contains (e.g., “This section is a recipe with steps,” or “This block is an FAQ answer to a known question”). In short, metadata and schema help your content get properly recognized as a high-quality, credible source by AI systems .
Internal Linking and Content Hierarchy (Internal linking for AI SEO & Topical authority for LLMs)
How your content connects within your own site also affects LLM comprehension. Strong internal linking and a logical site hierarchy can signal that you have topical authority and a wealth of related information:
- Link related content together: When you have multiple pages on related subtopics, link them contextually. For example, a pillar page about “AI in Marketing” might link to subpages on AI SEO, AI content tools, case studies, etc. This builds an “expertise map” of your site that LLMs (and search engines) can follow. A page that sits in a well-linked cluster of content is likely seen as more authoritative on that topic.
- Maintain a clear hierarchy: Use a sensible site structure (categories, sections) so that even if an AI crawler finds one page, it can easily navigate to your other relevant pages. For instance, ensure your important guides are not buried several clicks deep without links. A flat, logical architecture with clear navigation menus or breadcrumb trails can improve crawlability and context. LLMs may treat a page referenced by many other pages on your site as a “cornerstone” resource.
Internal linking can improve LLM extractability by providing more context about entities and relationships between concepts. In essence, a good internal link structure guides LLMs through your content just as it does for users , building a case that your site covers the topic thoroughly.
Page Performance and Accessibility (Crawlability & Access)
No matter how great your writing is, LLMs can only use what they can crawl and parse , a foundational requirement for LLM optimization . Technical barriers like slow load times, heavy scripting, or inaccessible media can prevent AI from consuming your content fully. Key considerations:
- Site speed and load accessibility: Ensure your page loads quickly and its content is readily available in the HTML. AI agents may not wait long for a response and may not execute complex client-side scripts. If your key content is hidden behind a slow script or only appears after user interaction, the crawler could miss it. Optimize images and code , use efficient servers/CDNs, and prefer static or server-rendered content for critical text.
- Mobile-friendliness and clean HTML: Use responsive design and standard HTML semantics. Valid, well-formed HTML with proper tags (heading tags, list tags, table tags, etc.) makes it easier for models to parse content structure. Also, ensure text is not baked into images (if it is, provide alt text or captions that the AI can read).
- Accessibility features: Implement accessibility best practices like alt text for images, ARIA labels for complex elements, and descriptive link text . These not only aid users with disabilities but also help AI. For instance, alt text can describe an infographic or chart, so the LLM knows what data it conveys.
- LLM crawlability checks: Confirm the page returns a 200 status, isn’t blocked by robots/noindex, loads the main content in the initial HTML (not only after JS), and isn’t gated by cookie/geo/login walls.
- OpenAI uses different crawlers for different purposes (for example, GPTBot for training, OAI-SearchBot for search/indexing, and ChatGPT-User for user-initiated retrieval).
- Anthropic uses different bots as well (for example, ClaudeBot for training, Claude-SearchBot for search/indexing, and Claude-User for user-initiated retrieval).
- Google-Extended is a special robots.txt token that controls whether content Google crawls can be used for Gemini training/grounding; it does not affect Google Search rankings.
- Don’t unintentionally block AI crawlers you want to allow: Review robots.txt (and any WAF/CDN bot rules) to ensure you’re not blocking the specific agents that matter for your goals. At a high level: Decide what you want (training inclusion, search visibility, or both), then allow/block accordingly.
LLMs cannot use what they cannot fetch . A slow or script-heavy page might get skipped in favor of a snappier source that delivers the content upfront. Ensuring your content loads quickly and plainly increases the likelihood that the AI captures your full message. In summary, speed, accessibility, and correct crawl permissions are prerequisites for all other optimizations.
Content Freshness and Maintenance
LLMs have an inherent training cutoff (for their base knowledge), but many can access current info via retrieval, and both cases favor fresh, up-to-date content . AI-driven overviews and assistants also tend to prioritize recent sources for time-sensitive queries. An outdated page is less likely to be selected by AI systems that prioritize recent knowledge for user queries. Best practices:
- Keep content updated: Regularly review and refresh your pages, especially statistics, references, or time-sensitive facts. If your article was published a while ago, add new insights from the past year or clarify that the info is still valid. Up-to-date content is a critical signal – models (and the algorithms feeding them) prefer not to serve stale or potentially incorrect info.
- Use timestamps and revision history: As mentioned, show last updated dates on the page. Some sites even include a brief change log for major updates. This transparency can be parsed by AI and certainly is noticed by users.
- Monitor and fix outdated elements: Set up a content audit routine (e.g., quarterly) to catch broken links, obsolete data, or declining engagement. From an AI standpoint, a page with obviously outdated info (say, an old year in the title or data that conflicts with newer facts found elsewhere) might be passed over by retrieval algorithms that aim to maximize factual correctness.
In fast-evolving topics, freshness correlates with accuracy . Even for evergreen topics, showing that a page is reviewed and upkept builds trust. Additionally, being current increases your chance of inclusion in future LLM training sets. OpenAI’s GPT-3, for example, drew ~60% of its data from the Common Crawl (filtered web) and ~22% from a WebText set of Reddit-linked pages. Pages that are fresh, frequently linked or discussed (and high-quality) have better odds of being swept into those datasets.
Summary of LLM-Friendly Page Practices
The table below summarizes major on-page elements and why they help with LLM comprehension:
On-page element | Anticipates user queries in a machine-friendly format; boosts the chance of a direct match to the query.
Clear headings & sections | Signals content structure to AI; allows accurate snippet extraction.
Short paragraphs & lists | Enhances readability for models; prevents important info from being buried.
TL;DR summary at top | Highlights the answer upfront; models often grab this for quick responses.
FAQ Q&A section | Ensures crawlers see all content (no heavy JS or delays); LLM can ingest the page fully.
Schema markup (Article/FAQ/etc.) | Provides machine-readable structure and context; improves disambiguation and citation opportunities.
Fast, text-first loading | Ensures crawlers see all content (no heavy JS or delays); LLM can ingest page fully.
Accessible design (alt text) | Allows AI to understand images/media; good HTML structure aids parsing.
Up-to-date information | Signals relevancy and accuracy; AI favors recently updated pages for current answers.
Authoritative tone & cites | Establishes credibility; factual statements with sources can be validated and trusted by AI.
In practice, LLM optimization aligns closely with good UX writing, AI-friendly content, and modern SEO, emphasizing clarity, relevance, structure, and credibility. Next, we’ll explore how these on-page factors, along with external signals, play into which pages LLMs choose to present in answer to a query.
2. Factors Influencing LLMs’ Selection and Citation of Webpages
Even if your page is perfectly optimized, an LLM still needs to find and trust it enough to use it. LLM-based answer systems (like ChatGPT Search/browsing, Microsoft Copilot, Perplexity, or Google AI Overviews/AI Mode) typically rely on a retrieval step – using either a search engine or a vector database – to fetch relevant content which the model will quote or consult. The exact algorithms are proprietary, but recent studies and observations reveal several key parameters that influence how LLMs select, prioritize, and cite webpages when answering questions. These parameters often mirror classic SEO factors (relevance, authority) but with important twists. Below are the major factors, internal and external, known or inferred to affect LLM source selection:
Relevance and Query Intent Alignment
Alignment with the user’s query intent is arguably the top factor. LLMs (and their retrieval modules) strive to find content that directly answers the question or fulfills the user’s intent, even if that content isn’t from the top of the traditional search rankings. In practice, this means a highly relevant niche page can outrank more general high-SEO pages in an AI answer. For example, in a Semrush study, nearly 90% of ChatGPT’s cited webpages were ones ranking below the top 20 in Google for the same query. This indicates the AI is zeroing in on pages that specifically answer the question, rather than those with broadly high PageRank. LLMs use their superior language understanding to match on semantic relevance: a page that might not be an SEO powerhouse but has a paragraph perfectly answering a nuanced question can be chosen because the model “knows” it’s a good fit.
Implication: Write content that meets specific needs and questions . If a user asks, “What’s the best CRM for a 5-person startup?”, a blog post titled “Best CRM for Small Teams: 5 Top Picks for Startups” with a focused answer can be selected by an LLM even if it’s not a top Google result. LLMs care about delivering the best answer for that exact query , not just the best overall website. Ensuring your content clearly addresses the intent (e.g., giving recommendations, not just definitions, if the query implies an advice intent) will align it with what the LLM is looking for in source material.
Page Structure and Answer Extractability (How content format affects LLM rankings)
The structural elements discussed in Part 1 (clear headings, concise passages, etc.) directly influence selection because they affect how easily the AI can extract a useful snippet. LLMs favor pages that are easy to scan for a self-contained answer . If a page is well-organized, the retrieval system can identify that one section is a direct answer to the question. In contrast, if the content is poorly structured or buries the answer in fluff, the AI might skip it for a page that presents the answer more plainly.
Concretely, features like a TL;DR, an FAQ, or clearly labeled sections increase a page’s chances. As noted earlier, adding a TL;DR summary can act as a beacon for the AI. Likewise, a cleanly formatted list of pros/cons or steps might be exactly what an LLM wants to provide to the user. In essence, a page that looks like it could have been written by an LLM (structured, concise, and on-point) is one that’s likely to be used by an LLM.
Implication: Invest in content formatting not just for human UX but for machine parsing. This includes using the correct HTML elements (for example, marking FAQ questions with heading tags like H3/H4 and answers in paragraph tags, and using proper list/table markup for steps and comparisons). One outcome of the Semrush research was that Google AI Overviews frequently pull from sites like Quora and Reddit – platforms that have a straightforward Q&A or threaded structure. Users ask clear questions and get distinct answers there, which the AI can repurpose. Ensuring your site’s content is comparably structured (clear question -> answer format) can put you on par with those Q&A sources in the eyes of the AI.
Trustworthiness and Source Authority (E-E-A-T for AI)
LLMs don’t inherently “know” which sites are authoritative the way a search engine’s rankings do, but they infer trust through multiple signals, effectively E-E-A-T for AI . These include both intrinsic content credibility (does the page have accurate, well-sourced info?) and extrinsic reputation (is the site/domain known and respected?). Many LLM retrieval systems likely incorporate or overlay traditional search engine rankings as one input, but they also look at other cues:
- Domain authority & content quality: If your site has a history of authoritative content, or if it’s a known entity (like a university site, a well-known publication, etc.), the AI may give it preference. For example, in Google’s AI Overviews, high-authority domains (NYTimes, NerdWallet, WebMD, etc.) often appear among cited sources. This suggests that Google’s system still values site reputation when choosing what to show in AI results. Similarly, LLM citation mechanisms often lean on sources like Wikipedia or official sites for factual queries, implying that those are considered trustworthy baselines.
- Experience/Expertise signals: These might include things like author bios (if the AI can detect them), site affiliation (an article on an official government or medical site likely carries weight), or even reviews/ratings in the content. While LLMs don’t see PageRank, they do notice content that reads as professional and trustworthy. A page that confidently provides evidence-backed answers in an expert tone is more likely to be chosen than one with speculative or salesy language. In fact, LLMs have been observed cross-referencing multiple sources to validate claims , favoring pages that align with the consensus. If your content stands out as dubious (e.g., making claims that conflict with widely trusted sources without acknowledgement), it might be passed over.
One particularly interesting finding: ChatGPT with browsing/search often cites business or service websites (about 50% of the time) when answering queries about those businesses or products. This means if someone asks about your company or product, the AI is likely to use your official website as a source – if that site provides the info in a clear, accessible way. In general, LLMs consider official or firsthand sources authoritative for factual info about themselves (e.g., company homepage for company data).
Implication: Building authority and trust is as crucial for AI as it is for traditional SEO – if not more. Ensure your content is factually accurate, well-written, and aligns with known trustworthy information. Incorporate elements that establish credibility (citations of your own, author credentials, about pages) where possible. This also means maintaining consistency : LLMs penalize contradictory information. If your product pricing is stated one way on one page and differently elsewhere, the AI might lose confidence. Strive for consistency and accuracy across your content.
Topical Authority and Site-Wide Context
Related to trust is the idea of topical authority : if your site (or section of site) is dedicated to a topic and covers it comprehensively, an AI might preferentially choose content from you for questions on that topic. This concept extends the internal linking point from earlier – it’s about the AI’s macro view of your content portfolio.
- Site expertise profile: LLMs, when crawling or training on your site, effectively build an internal representation of what your site is about. If you have many interrelated pages on a subject , the AI can form an “expertise graph”. For example, a tech blog that has dozens of articles on cybersecurity (and little else) may be seen as an authoritative node in the AI’s knowledge network for cybersecurity questions. When a cybersecurity query comes, the retrieval might favor that site’s pages (even if each individual page’s SEO metrics are average) because collectively it knows a lot about the topic.
- Entity consistency and knowledge graph presence: Modern AI models often integrate with knowledge graphs. Ensuring your brand or key content is represented in public knowledge bases (like Wikipedia, Wikidata, or Google’s Knowledge Graph) can reinforce your authority. If an LLM’s retrieval system recognizes, say, AcmeCorp as a known entity with a knowledge panel and multiple references, it might trust content from AcmeCorp’s site more for queries in its domain.
Implication: Aim to build topic clusters on your site and bolster your presence in official knowledge sources. Use schema markup to tie your content to defined entities (e.g., Organization with sameAs links to your LinkedIn or Crunchbase, or Person schema for authors). When your brand or site is an entity the AI recognizes, it can factor that into retrieval ranking.
External Mentions and Backlink/Reference Profile
Traditional SEO values backlinks; in the LLM era, the emphasis shifts to mentions and references across the web (“unlinked” or linked). Essentially, LLMs notice if your content or brand is being talked about by others , as it feeds into both training data and real-time retrieval confidence:
- Third-party mentions & corroboration: If multiple reputable sources refer to your content or reach similar conclusions, an LLM is more likely to trust and select your content. For example, if your site publishes a study and it’s cited by a few news articles or industry blogs, an LLM answering a question about that topic might preferentially cite the original study (your site) because it sees that information echoed elsewhere.
- Being featured on high-authority platforms: Getting content on Wikipedia, news outlets, academic journals, or popular Q&A forums can indirectly boost your visibility. These platforms themselves are frequented by LLMs (either in training or for real-time answers). A Semrush analysis found Quora is the #1 cited domain in Google’s AI Overviews, with Reddit second. It suggests that content that lives on or is referenced by these community-driven sites is more likely to surface. Similarly, if your site is mentioned in a “Top 10 tools” list on a high-authority blog , an AI might pick up on that mention when compiling an answer about your category, thereby finding you.
Implication: Cultivate a robust off-site presence . This can mean digital PR (getting your data or experts quoted in news articles), guest posting, participating in forums, or sponsoring studies – anything that gets your brand/content mentioned in diverse, authoritative places. Not only do such mentions signal credibility (which some AI retrieval scoring likely factors in ), they also increase the chances your content is part of the training data or gets picked up by specialized searches.
Freshness and Recency Signals
We discussed keeping your own content fresh; when it comes to AI selecting sources, recency is often a deciding factor , especially for newsy or evolving queries. LLMs integrated with web search will typically favor a more recent article over an older one if both are relevant, to minimize the chance of outdated info. Google AI Overviews, for instance, have been seen citing very recent articles (from the same day or week) for topics like breaking news or recent product releases – areas where freshness is critical.
- Frequency of updates: Sites that update frequently or cover timely topics may be crawled more often. Publishing updated XML sitemaps and RSS feeds, and using visible update timestamps, helps discovery.
- Content age and query type: If a user asks, “What are the latest COVID-19 travel restrictions?”, an AI should preferentially cite a very recent source (past few weeks). If your page on that topic was updated yesterday and others were last updated a year ago, yours has a big edge. On the other hand, for a timeless question (e.g., a math formula), recency is less important than accuracy.
Implication: Make sure to broadcast your content updates – via sitemaps, RSS feeds, and update timestamps – so that retrieval systems know your page is fresh. Keeping your content updated and emphasizing its newness (like using “2026” in titles where appropriate) can be a deciding factor for being the cited source in an AI’s answer.
Presence on Curated and High-Authority Sources
LLMs often draw from curated knowledge bases and reputable sites as a baseline. We touched on Wikipedia and knowledge graphs under topical authority, but it’s worth highlighting: if your information appears on Wikipedia, Wikidata, or major news outlets, it significantly boosts your credibility to an AI. These are considered canonical sources.
So, if the question is, “What is Company X’s revenue?” and your site says one number but Wikipedia (with a citation) says another, the AI will likely go with Wikipedia’s (or at least be uncertain about yours). Conversely, if your site is the source feeding those outlets (e.g., your press release is cited on Wikipedia or reported in TechCrunch), then your information becomes part of the trusted canon.
Another curated source category is datasets and official documentation . For example, if you publish an official API or dataset and it’s referenced on data portals or GitHub, an AI might use it to answer queries requiring those data points.
Implication: Strive to get your facts into the trusted public sphere . That could mean contributing to Wikipedia (with neutrality and citations), ensuring journalists or analysts have correct info (so that news articles reflect your data), and maintaining accurate info in knowledge panels (Google Business Profile, etc.). Also, use structured formats – for example, publishing key facts in a CSV/JSON on your site that others can easily incorporate.
Schema Markup and Machine Signals
We already covered schema in on-page factors, but to reiterate its role in selection: by using schema markup, you make it easier for retrieval algorithms to identify the relevance of your page. For example, if a user asks a how-to question and your page has HowTo schema, an AI service might filter for pages with that schema (knowing they likely contain step-by-step instructions). Similarly, the FAQ schema and a clean Q&A block can make it easier to pull a direct answer.
Implication: Use schema tactically to map to query intent. If you have content that suits a certain intent (how-to, FAQ, definition, tutorial, product info, etc.), mark it up accordingly so the AI can recognize that and consider your page. This also extends to less common schema types that indicate high-quality info: for instance, the DefinedTerm schema for definitions, the Dataset schema for original data, or the ScholarlyArticle for research content.
Factual Accuracy and Consistency
Lastly, a crucial inferred factor: the factual precision of your content . LLMs don’t want to cite incorrect information. There is evidence that models will sometimes cross-check information across sources if possible. An LLM or its retrieval subsystem might down-rank content that has known factual errors or contradicts verified facts. For example, if 9 sources say one thing and yours says something else with no support, the AI may avoid your content. On the flip side, if you offer unique facts, but with clear evidence , you can become a go-to source.
A peer-reviewed study found that LLMs can fabricate citations or cite incorrect sources, which is exactly why having verifiable facts on your page matters: the AI can check and see whether your statements match other data. The movement in AI is toward reducing hallucinations , which increases reliance on content with solid evidence.
Implication: Double down on accuracy . If feasible, cite sources within your content for key facts (the AI might actually read your citations list or references – it certainly notices quotes and numbers). Being consistent (no self-contradiction) and correct builds a track record.
Summary of Key LLM Ranking Factors
The table below outlines major parameters influencing LLMs’ selection of webpages, and their effects:
Factor | How it affects LLM citation/selection
Query intent match | Information present on Wikipedia, major news outlets, or popular Q&A sites is more likely to be used. LLMs heavily cite community and high-authority domains (e.g., Quora, Reddit, mainstream news) as sources.
Content structure & clarity | Well-structured pages (clear sections, lists, summary) are easier for AI to parse and quote. Clean formatting boosts “extractability,” so such pages are more likely to be chosen for answers.
Source authority (E-E-A-T) | Trusted domains or authors (official, expert, or widely recognized sources) get preference. Content aligning with known facts (Wikipedia, etc.) carries more weight. High expertise and consistent accuracy improve a page’s trustworthiness to LLMs.
Topical authority | Sites with depth in a topic (many interlinked pages on the subject) are seen as authoritative hubs. LLMs tend to pull from these “authority clusters” for related questions. A strong presence in knowledge graphs (Wikidata, etc.) further boosts trust.
External validation | Content that’s corroborated or referenced by multiple independent sources is considered reliable. Repeated mentions across reputable sites (news, forums, academic) reinforce credibility.
Freshness | Recent content is prioritized for queries where information changes over time. LLM systems favor pages with recent update timestamps for up-to-date answers.
Presence on key platforms | Information present on Wikipedia, major news outlets, or popular Q&A sites is more likely to be used. LLMs heavily cite community and high-authority domains (e.g. Quora, Reddit, mainstream news) as sources.
Structured markup | Pages with schema.org metadata (FAQ, HowTo, etc.) can be identified and retrieved more precisely for relevant queries. Structured data also adds disambiguation and trust.
Crawlability & access | If an AI crawler can’t access the page (due to robots.txt or paywalls), it won’t be selected. Pages that are machine-accessible make it easier for LLMs to include their content.
Factual precision | Pages with accurate, specific facts (especially if unique or exclusive) will be chosen over vague or dubious ones. LLMs aim to avoid incorrect info, so a reputation for accuracy (and providing evidence) improves selection likelihood.
It’s important to note that these factors often intersect . For example, a page on a high-authority site that is also fresh and well-structured hits multiple marks and is highly likely to be cited. On the other hand, a page might excel in one area but not others (e.g., extremely relevant content but on an obscure site with no external mentions). In such cases, the retrieval system balances signals. The ideal scenario is to cover as many of these bases as possible – create highly relevant, well-structured, factual content on a trusted, frequently updated site that others cite – to maximize the chances an LLM will surface and credit your page.
Frequently Asked Questions (FAQ)
Provide concise, citable chunks (TL;DR, FAQs), mark them up with Schema.org, keep pages crawlable and fresh, and earn corroborating mentions. There’s no guarantee, but these steps materially increase the likelihood of selection.
Optimize for general AI extractability, and you’ll cover most of what matters for specific LLMs. The fundamentals are stable across systems: clear structure, entity clarity, evidence, crawlability, and freshness. The LLM-specific layer is mostly about: Answer-first formatting (so they can quote you) Factual grounding (so they trust you) Clear entity language (so they don’t misattribute)
Use explicit nouns and entity names, define acronyms, and make each section understandable without the rest of the page. Avoid references like “this”, “it”, or “they” when it’s unclear what those refer to. A simple test: if you copied one paragraph into a document by itself, would it still make sense?
Yes. Internal links provide context and relationships between topics (definitions, supporting articles, related use cases). This helps retrieval systems understand what a page is “about” and can reinforce topical authority.
Content that presents clear, structured, concise, and verifiable information that a model can extract and cite: descriptive headings, short paragraphs, lists/tables, appropriate schema, and accurate facts.
Schema reduces ambiguity about entities and page sections so retrieval can match intent and extract the right snippet. FAQPage maps questions to answers; Dataset/HowTo/DefinedTerm markup can increase precision when models interpret your page.
Include 6-10 natural-language questions with brief, factual answers near the end of the page. Mirror common queries verbatim, keep answers self-contained, and (optionally) mark up the section with FAQPage schema.
Focus on LLM optimization (GEO): lead with a TL;DR, clear headings, and a short FAQ; add Article/FAQPage schema; ship fast, text-first pages with verifiable facts and citations. For crawl controls, be precise about which bot you mean: If you want visibility in ChatGPT Search , make sure you’re not blocking OAI-SearchBot . If you want visibility in Claude’s search experiences , make sure you’re not blocking Claude-SearchBot . Decide separately whether you want to allow training crawlers like GPTBot and ClaudeBot .
Use answer-first formatting (TL;DR, clear headings, lists/tables), keep facts current, and apply intent-matching schema (HowTo/FAQ/Article). Build topical authority with internal links and provide strong E-E-A-T signals. Also, make sure your content is eligible for retrieval: fast to load, indexable in Google Search, and not blocked by accident.
Check both what the model says and what your site signals : Prompt test: ask the same question in multiple tools (Gemini, ChatGPT, Claude, Perplexity) and see whether your page is cited. Snippet test: confirm your page contains a clean, self-contained paragraph that answers the query directly. Crawl test: verify robots.txt and server logs to confirm the relevant crawlers can fetch the page. Consistency test: make sure key facts (pricing, definitions, dates) match across your site.
If you’re evaluating LLM optimization companies or tools, look for help with the fundamentals (content + technical), plus measurement: Content systems: a repeatable process to produce answer-first pages, comparisons, FAQs, and definitions that map to real queries. Technical SEO + crawlability: schema implementation, indexation hygiene, and bot access controls (training vs search vs user-initiated retrieval). Entity and brand clarity: consistent naming, knowledge graph alignment, and cited first-party sources for your key facts. Measurement: tracking citations/mentions in AI products, prompt-based testing, and tying improvements to business outcomes. Avoid anyone promising guaranteed rankings or citations-LLM and AI Overview sourcing is probabilistic and can change quickly. Start by tightening on-page extractability (structure, evidence, schema), then build authority and distribution so your brand shows up across the broader web.
