LLM SEO (Large Language Model SEO), also called GEO (Generative Engine Optimization), is the practice of optimizing your brand’s digital presence so that AI-powered tools like ChatGPT, Perplexity, Google AI Overviews, and Gemini cite your brand in their generated answers.
The shift matters because AI search doesn’t just rank pages. It synthesizes answers. When a potential customer asks an AI tool “What’s the best project management tool for remote teams?” or “Which CRM should a B2B startup use?”, the brands cited in that answer gain visibility that traditional search rankings alone no longer provide. This isn’t replacing SEO. It’s extending it for interfaces where users never see a traditional search results page.
This guide offers a clear, practical explanation of LLM SEO: what it is, why it matters, and what marketing leaders should do about it.
Key points:
- AI search delivers synthesized answers with citations, so visibility means being cited, not just ranking
- Research from Princeton University shows GEO optimization can boost visibility by up to 40% in AI responses
- Traditional search ranking and AI citation correlate but aren’t identical: ranking #1 doesn’t guarantee AI visibility
- Success is measured by “share-of-answers,” meaning how often you’re cited for relevant queries
- Foundational changes typically show measurable results within 60–90 days
How AI Search Works
Traditional search and AI search operate on fundamentally different principles. In traditional search, an algorithm ranks pages by relevance and authority, then users click through to find their answers. In AI search, the model synthesizes an answer directly from multiple sources, sometimes citing them, sometimes not. This changes what “being found” means. Success is no longer about ranking on page one. It’s about being included in the synthesized response itself.
Two broad mechanisms determine whether your brand appears in AI answers. The first is training data influence, which reflects what the model learned during development. This includes your historical brand authority and mentions across the web. The second is retrieval-augmented generation (RAG), where AI systems pull from external sources to inform their responses. Retrieval can come from multiple source types depending on the platform and query: web indexes, knowledge bases, licensed content, or partner data. The second mechanism is more directly influenceable through your current content and optimization efforts.
Citation behavior varies significantly across platforms, both in how often systems cite sources and which sources they prefer. In a controlled news citation retrieval test, Columbia University’s Tow Center found that Perplexity had the lowest failure rate at 37%, while other platforms showed higher failure rates, with some exceeding 90% (Jaźwińska & Chandrasekar, March 2025). These findings come from a specific testing methodology focused on news citations, so they illustrate platform differences rather than universal citation accuracy rates.
Platform-specific citation patterns also differ in terms of source preferences. Analysis by Profound across 680 million citations found that Wikipedia accounts for 7.8% of total ChatGPT citation volume. Within Perplexity’s top-10 most-cited sources, Reddit dominates at 46.5% of that top-10 share (Profound, 2025). These are different metrics: overall volume versus concentration within top sources. Understanding both helps explain why optimization strategies must account for platform variation.
How Is LLM SEO Different from Traditional SEO?
Marketing leaders often ask whether LLM SEO is just traditional SEO with a new name. It isn’t, but it’s not a complete departure either. Understanding the relationship helps clarify what new capabilities you need.
| Dimension | Traditional SEO | LLM SEO |
|---|---|---|
| Goal | Rank on search results page | Get cited in AI-generated answers |
| Success metric | Rankings, organic traffic, CTR | Share-of-answers, citation frequency |
| Content focus | Keyword-optimized pages | Answer-first content, entity clarity |
| Technical focus | Page speed, backlinks, crawlability | Structured data, entity markup, knowledge graph |
| Content style | Can include promotional language | Performs best when factual and evidence-based |
| Third-party signals | Backlinks from high-authority domains | Citable mentions on independent trusted sources |
The relationship between traditional rankings and AI citations is real but imperfect. According to Ahrefs analysis of 1.9 million citations from 1 million AI Overviews, approximately 76% of AI Overview citations come from pages ranking in Google’s top 10 results, with a median ranking of position 3 for cited pages (Ahrefs, August 2025). Traditional SEO success increases your odds of AI citation. However, roughly 24% of citations come from pages outside the top 10, and ranking well doesn’t guarantee citation.
A Seer Interactive study reinforces why this matters: organic CTR for queries featuring AI Overviews fell 61% between mid-2024 and late 2025. Even queries without AI Overviews saw a 41% CTR decline (Seer Interactive, November 2025). Users are clicking less across all search contexts, which makes citation visibility increasingly important.
The implication for marketing leaders: LLM SEO builds on traditional SEO foundations. You still need crawlable, authoritative content. But those foundations alone are no longer sufficient. Entity optimization, structured data, and answer-oriented content now determine whether you’re visible in the growing segment of search that happens through AI interfaces.
What Does LLM SEO Actually Involve?
The foundational research on GEO comes from a 2024 study by researchers at Princeton University, Georgia Tech, Allen Institute for AI, and IIT Delhi. Their experiments demonstrated that specific content optimization methods can boost visibility by up to 40% in generative engine responses (Aggarwal et al., KDD 2024). Here’s what the research and industry practice tells us works.
Entity Optimization and Structured Data
Entity optimization means establishing your brand as a clearly defined, disambiguated entity that AI systems can recognize and categorize. This involves implementing Schema.org structured data and ensuring consistent brand information across web properties.
Regarding Wikipedia and Wikidata: if your brand already has a Wikipedia page, ensure the citations and facts are accurate and current. For Wikidata, verify your brand’s entry exists and contains correct information. However, don’t attempt to create a Wikipedia page if your brand doesn’t meet notability guidelines. Wikipedia has strict conflict-of-interest policies, and attempting to manufacture notability can backfire reputationally. Focus on building genuine authority that might eventually warrant a Wikipedia presence organically.
Research on structured data’s impact shows mixed but generally positive results. A BrightEdge study found that schema markup improved brand presence in Google’s AI Overviews, with higher citation rates on pages with robust structured data implementation (cited in Search Engine Journal, September 2025). A controlled experiment published in Search Engine Land found that only pages with well-implemented schema appeared in AI Overviews during testing, suggesting schema quality, not just presence, may influence visibility (Nogami & Benjamin, September 2025).
However, a Search Atlas study analyzing citation patterns across OpenAI, Gemini, and Perplexity found that schema markup alone did not correlate with higher LLM citation frequency (Search Atlas, December 2025). The relationship likely depends on platform, query type, and implementation quality rather than schema presence alone.
What we’ve learned at Ameus: In audits across B2B SaaS and professional services brands, we’ve found that entity inconsistency is the most common gap. Brands typically have correct Schema.org markup on their homepage but contradictory information elsewhere: different founding dates on Crunchbase, outdated descriptions on LinkedIn, inconsistent product naming on G2 listings. AI systems appear to weight consistency across sources when determining citation confidence. Fixing these discrepancies often produces faster visibility improvements than creating new content. One client saw measurable citation gains within 45 days simply by aligning their entity information across 12 platforms, with no content changes at all.
Answer-First Content Structure
The Princeton GEO research tested nine different optimization methods and found that content quality and structure significantly impact citation likelihood. Methods that improved fluency and readability showed 15–30% visibility improvements. Adding citations to relevant sources, including quotations from experts, and incorporating statistics all significantly boosted source visibility (Aggarwal et al., 2024).
This translates to practical content guidance. Start important pages with clear, direct answers to the questions they address. Use short sentences and standard terminology. Support claims with evidence: numbers, dates, and sources. Structure content so AI systems can easily extract and cite relevant passages.
The research also found that simple keyword optimization didn’t work well. AI systems evaluate content quality, not just keyword presence. Content that performs best for AI visibility tends to be factual, specific, and citation-friendly rather than promotional or vague.
What we’ve learned at Ameus: The most common mistake we see is treating LLM SEO as a content creation project. Brands want to create new “AI-optimized” pages instead of restructuring existing high-authority content. But AI systems already know which domains have authority. Restructuring your best-performing traditional SEO pages for answer-first format typically outperforms creating new content from scratch. We had a client with a comprehensive pricing guide ranking #2 for a competitive term. The page had strong authority but buried the actual pricing information under 800 words of context. Restructuring it to lead with a clear pricing summary improved AI citation rates for pricing-related queries within six weeks. No new content was needed.
Third-Party Citations and Source Authority
AI systems heavily weight source authority and consensus when deciding what to cite. Analysis by Surfer SEO of 46 million AI citations found that YouTube, Wikipedia, and Google.com are the most frequently cited domains across industries (Surfer, October 2025). According to Semrush data, Reddit is the single most cited website across major LLMs at approximately 40% of total citations, followed by Wikipedia at 26% (Semrush, November 2025).
This concentration has important implications. AI systems trust and cite established, authoritative sources disproportionately. For brands, third-party mentions matter significantly. Appearing in comparison guides, industry publications, expert roundups, and community discussions increases your citation likelihood.
Interestingly, the Princeton research found that websites ranking lower in traditional search results benefit more from GEO optimization than those already ranking highly. The “Cite Sources” optimization method led to a 115% increase in visibility for websites ranked fifth in search results, while top-ranked websites saw an average 30% decrease from the same method (Aggarwal et al., 2024). This suggests GEO may help level the playing field for smaller or newer brands.
What we’ve learned at Ameus: Third-party citation building for AI visibility differs meaningfully from traditional link building. The goal isn’t anchor text optimization or PageRank transfer. It’s creating extractable, neutral mentions that AI systems will trust as evidence. We’ve tracked queries where a brand appeared in zero AI responses despite having dozens of backlinks, then appeared consistently after being mentioned in a single well-structured comparison article on an authoritative industry site. The comparison article included a clear one-sentence description of what the brand does, which AI systems extracted almost verbatim. Quality and extractability matter more than quantity.
Monitoring and Freshness
AI systems update continuously, and citation patterns change as models evolve. Content freshness signals, including publication dates, update timestamps, and recent information, appear to influence how AI systems weight sources, particularly for queries where timeliness matters. Regular monitoring of your brand’s AI visibility across platforms helps identify what’s working and where gaps exist.
What we’ve learned at Ameus: Most brands underestimate citation volatility. We’ve seen share-of-answers swing 25% week-to-week for the same queries with no changes to underlying content. Model updates, retrieval index refreshes, and competitive content changes all create noise. Clients who check AI visibility once and treat it as a fixed baseline get frustrated when results fluctuate. We recommend weekly spot-checks on a core query set during active optimization, then monthly monitoring once a baseline is established. The goal is trendlines over 3–6 months, not snapshots.
What LLM SEO Cannot Control
Understanding limitations is essential for realistic planning. Several factors remain outside direct optimization influence:
Personalization and variance. AI responses vary based on user context, conversation history, account settings, and sometimes geographic location. The same query can produce different citations across sessions, users, or days.
Model updates. AI platforms update their models regularly, sometimes dramatically changing citation behavior. Optimization that works today may need adjustment as systems evolve.
Citation accuracy issues. As the Tow Center research showed, AI systems frequently produce inaccurate or hallucinated citations. Your brand might be cited incorrectly, or competitors might receive citations they don’t deserve. This is a platform-level problem, not something individual optimization can solve.
Branded versus generic queries. AI systems behave differently for branded queries (where users ask about a specific company) versus generic category queries. Optimization primarily affects generic queries where AI must choose among multiple potential sources.
Platform-specific behavior. What works for Perplexity may not work for ChatGPT or Google AI Overviews. Each platform has different retrieval mechanisms, source preferences, and citation patterns. Cross-platform consistency is difficult to achieve.
These limitations don’t make LLM SEO pointless. They mean expectations should be realistic and measurement should focus on trends rather than absolute numbers.
When Does LLM SEO Matter Most?
LLM SEO isn’t equally urgent for every brand. Understanding where it delivers the most value helps you prioritize appropriately.
Advantages of investing in LLM SEO:
- Reaches users who get answers without ever seeing traditional search results
- Builds on existing SEO work rather than requiring a complete restart
- Early movers gain compounding visibility as citation presence reinforces itself
- Research shows optimization can improve visibility by up to 40%
Current limitations to consider:
- Measurement tools are still maturing, and manual auditing remains necessary
- Platform behaviors change as AI models update, requiring ongoing attention
- Citation accuracy varies widely by platform (37–94% failure rates in Tow Center testing)
- Requires sustained effort, not one-time optimization
LLM SEO should be a high priority if your audience includes early adopters of AI tools (common in tech, B2B software, professional services), if your category involves considered purchases where people research before deciding, or if competitors already appear in AI answers while you don’t. It’s a lower priority if your site has major technical SEO issues that need fixing first, if discovery happens primarily through non-search channels like referrals or events, or if you haven’t yet built foundational content that demonstrates expertise.
How to Start: A Practical Roadmap
The following steps provide a starting framework. Each builds on the previous, so the sequence matters.
Step 1: Audit your current AI visibility. Ask ChatGPT, Perplexity, Gemini, and Google AI Overviews 10–15 questions your customers would realistically ask when researching your category. Document which brands are cited in each response and note whether you appear. Pay attention to how you’re described when cited, since accuracy matters. This takes 1–2 hours and provides immediate clarity on where you stand relative to competitors.
Step 2: Assess your entity clarity. Search for your brand in Google’s Knowledge Panel to see how Google understands your entity. Test your structured data implementation using Google’s Rich Results Test. Check whether your brand has a Wikidata entry and whether the information is accurate. Then check for consistency: does your Crunchbase profile match your LinkedIn company page? Does your G2 listing use the same product names as your website? Inconsistencies create ambiguity that reduces citation confidence.
Step 3: Evaluate your content structure. Review your ten most important pages. Does each page begin with a clear, extractable answer to the question it addresses? Are claims supported by evidence such as statistics, citations, and expert quotes? The Princeton research found these elements significantly improve citation likelihood. Is the tone factual and evidence-based rather than primarily promotional?
Step 4: Map your third-party citation landscape. Given that AI systems heavily cite Wikipedia, Reddit, YouTube, and established publications, check whether your brand appears in these contexts for relevant queries. Search for “[your category] best tools” and “[your category] alternatives” in AI tools and note which sources are cited. This reveals both which publications matter for your category and where you have gaps.
Step 5: Build a prioritized 90-day plan. Based on your audit findings, prioritize: entity consistency and structured data provide foundational clarity, content restructuring improves citation likelihood for high-value pages, and third-party citation building takes longer but compounds over time. Set specific monthly milestones and plan to re-run your visibility audit at 30, 60, and 90 days to measure progress.
Measuring LLM SEO: Practical Considerations
The core metric for LLM SEO is “share-of-answers”: the percentage of relevant queries where your brand is cited across AI platforms. However, measurement in this space requires realistic expectations.
Treat this like brand tracking, not performance marketing. AI outputs vary significantly based on factors you can’t control: user context, model updates, retrieval timing, and platform-specific behavior. A query that cites you today might not cite you tomorrow, even with no changes to your content.
Use consistent methodology. Establish a fixed set of 20–30 queries relevant to your brand and category. Test them across the same platforms using the same approach (logged out, cleared context, consistent phrasing). Run these tests monthly. The goal is trend data over time, not absolute numbers from any single measurement.
Distinguish citation types. Being mentioned is not the same as being cited as a primary source. Track whether you appear as the main recommendation, a supporting reference, or a passing mention. Also track citation accuracy: are you described correctly?
Accept variance as normal. If your share-of-answers fluctuates 10–20% month to month with no changes to your optimization, that’s typical platform behavior, not a measurement error. Look for sustained trends over 3–6 months rather than reacting to individual data points.
Frequently Asked Questions
What’s the difference between LLM SEO and GEO?
These terms refer to the same practice. LLM SEO (Large Language Model SEO) and GEO (Generative Engine Optimization) are used interchangeably across the industry. The term GEO was introduced in the 2024 Princeton research paper that established the foundational framework for this field (Aggarwal et al., KDD 2024).
How long until I see measurable results?
Foundational changes like entity consistency fixes and content restructuring typically show measurable citation improvements within 60–90 days as AI systems recrawl and reindex content. The Princeton research showed visibility improvements of up to 40% from optimization methods. Building meaningful third-party citation presence takes longer, usually 3–6 months.
Does traditional SEO still matter?
Yes, substantially. Ahrefs data shows 76% of AI Overview citations come from pages in Google’s top 10 results (August 2025). Traditional search ranking correlates strongly with AI citation likelihood. LLM SEO builds on traditional SEO foundations rather than replacing them.
Is promotional content bad for LLM SEO?
AI systems tend to favor neutral, evidence-based content when selecting sources to cite. The Princeton research found that fluency, readability, and factual grounding improve visibility, while keyword stuffing doesn’t help. Promotional language isn’t automatically disqualifying, but content needs to provide extractable, verifiable information to be cited.
Which sources do AI systems cite most?
According to Semrush data, Reddit is the most cited website across major LLMs at approximately 40%, followed by Wikipedia at 26% and YouTube at 24% (November 2025). Citation patterns vary by platform and query type. For Google AI Overviews specifically, pages from the top 10 search results receive 76% of citations (Ahrefs, August 2025).
Can small brands compete with established players in AI citations?
The Princeton research suggests yes. Lower-ranked websites benefit more from GEO optimization than those already ranking highly. The “Cite Sources” method produced a 115% visibility increase for fifth-ranked sites versus a 30% decrease for top-ranked sites. Optimization may help level the playing field.
Moving Forward
AI search visibility compounds. The brands being cited today build authority that makes them more likely to be cited tomorrow. Waiting means competitors establish presence while you play catch-up.
The foundational research demonstrates that optimization works: content improvements can boost visibility by up to 40%. But the field is evolving rapidly, measurement requires patience, and results vary across platforms and over time. This isn’t a set-it-and-forget-it channel. It’s a new dimension of visibility that rewards sustained attention.
The starting point is simple: understand where you stand today. Everything else follows from that baseline.
Want to see how your brand appears in AI search? Request a LLM visibility report. We’ll analyze your presence across ChatGPT and Google AI Overviews and identify the specific opportunities most relevant to your situation.
This guide was written by the Ameus team. We’re a specialized LLM SEO agency focused on helping B2B and SaaS brands improve their visibility in AI-generated answers. For questions or to discuss your brand’s AI visibility, contact us at hi@ameus.ee or visit ameus.ai.
Sources
- Aggarwal, P., Murahari, V., Rajpurohit, T., et al. (2024). “GEO: Generative Engine Optimization.” KDD 2024, Proceedings of the 30th ACM SIGKDD Conference. Princeton University, Georgia Tech, Allen Institute for AI, IIT Delhi. https://arxiv.org/abs/2311.09735
- Ahrefs (August 2025). “76% of AI Overview Citations Pull From Top 10 Pages.” https://ahrefs.com/blog/search-rankings-ai-citations/
- Jaźwińska, K. & Chandrasekar, A. (March 2025). Tow Center for Digital Journalism, Columbia University. Reported in Nieman Journalism Lab. https://www.niemanlab.org/2025/03/ai-search-engines-fail-to-produce-accurate-citations-in-over-60-of-tests-according-to-new-tow-center-study/
- Profound (2025). “AI Platform Citation Patterns.” Analysis of 680 million citations, August 2024–June 2025. https://www.tryprofound.com/blog/ai-platform-citation-patterns
- Seer Interactive (November 2025). “Google AI Overviews drive 61% drop in organic CTR.” Reported in Search Engine Land. https://searchengineland.com/google-ai-overviews-drive-drop-organic-paid-ctr-464212
- Semrush (November 2025). “26 AI SEO Statistics for 2026.” https://www.semrush.com/blog/ai-seo-statistics/
- Surfer SEO (October 2025). “AI Citation Report 2025.” Analysis of 36 million AI Overviews and 46 million citations. https://surferseo.com/blog/ai-citation-report/
- Search Engine Journal (September 2025). “Structured Data’s Role In AI And AI Search Visibility.” Citing BrightEdge research. https://www.searchenginejournal.com/structured-datas-role-in-ai-and-ai-search-visibility/553175/
- Search Engine Land (September 2025). “Schema and AI Overviews: Does structured data improve visibility?” https://searchengineland.com/schema-ai-overviews-structured-data-visibility-462353
- Search Atlas (December 2025). “The Limits of Schema Markup for AI Search.” https://searchatlas.com/blog/limits-of-schema-markup-for-ai-search/
