GEO & AEO: how to get cited by ChatGPT, Gemini, and YandexGPT — full guide

SEO, AEO, and GEO: three different jobs

TL;DR: SEO chases rankings, AEO chases direct answers in SERPs, GEO chases brand mentions inside LLM answers—three jobs, three metric stacks.

Each acronym is a different surface where your brand can show up. Knowing the split tells you where to invest.

SEO — Search Engine Optimization

Classic optimization for Google, Yandex, Bing: earn a spot in the link list and win the click—titles, H1s, links, speed, CWV. Still essential, but alone it’s incomplete without GEO/AEO.

AEO — Answer Engine Optimization

Optimization for direct answers: featured snippets, Yandex Alice blocks, Siri, Assistant. One source is quoted verbatim or close. You need crisp, structured answers to explicit questions.

GEO — Generative Engine Optimization

Optimization for generative models—ChatGPT, Gemini, Claude, Perplexity, YandexGPT, GigaChat, DeepSeek. Unlike AEO, the model synthesizes across sources, paraphrases, and cites links. Your job is to be one of those sources with a clear brand mention.

Why SEO alone is no longer enough

TL;DR: Strong SEO no longer guarantees demand—users get shortlists from ChatGPT and may never open ten blue links, so GEO covers pre-click demand.

Picture this: top 3 in Google on 80% of target queries, technical SEO at 95%, weekly publishing—and organic traffic still drops ~30% in six months.

This isn’t hypothetical—many B2B SaaS teams saw it in 2025. The driver wasn’t a random Google update. Users stopped clicking links the same way.

Now someone asks ChatGPT “which analytics tool should marketers pick?” and gets a ranked list with rationale—no ten-tab research. If you aren’t named, the buyer may be gone before they ever load your site.

Case study: B2B SaaS went from zero to top-3 AI answers in 60 days

Context: email-automation tool, 70+ competitors, zero ChatGPT/Perplexity mentions at baseline.

What they did in 60 days:

1. Unblocked GPTBot and PerplexityBot in robots.txt—blocked “just in case” since 2023.
2. Rewrote eight pillar pages answer-first: definition up top, FAQ blocks, Schema markup.
3. Published three independent placements: a tool comparison, a customer case, and a guest article.
4. Shipped Article + FAQPage JSON-LD on every updated URL.

Results after 60 days

• ChatGPT mentions: 0 → 23 of 50 test prompts
• Recommendational prompts: top-3 in 14 of those 23
• perplexity.ai referrals: +340% vs baseline
• Branded queries in GSC: +67% over the period

Search isn’t dead—it evolves. Seer (1.8M sites, 2025): 74% of brands in Google’s top 10 also appear in ChatGPT; correlation ~0.65. SEO is the foundation; GEO is the layer on top.

How GEO shapes traffic: three channels standard analytics miss

TL;DR: GEO lifts referrals, branded search, direct, and word of mouth; most impact won’t show up as a neat chatgpt.com referral row.

Most GEO impact never shows up as a direct referral. The model recommends a brand—the user remembers and arrives later via search, direct, or another channel.

How we got here: a short history of GEO

TL;DR: GEO follows zero-click SERPs and AI Overviews: surfaces answer first, then answers move into conversational models.

GEO isn’t a 2024 marketing fad—it’s the next stage of search. Understanding the timeline shows where things are heading.

How a model chooses whom to cite: the RAG architecture

TL;DR: Models shortlist sources on five signals—crawl access, answer-first structure, E-E-A-T, semantic density, and schema; the most ignored gate is robots.txt (~35% of RU sites still block AI crawlers).

To act with intent, understand the mechanics. Modern AI search (ChatGPT Search, Perplexity, YandexGPT) is mostly RAG—retrieval first, then generation.

It looks like “the model found the best sources,” but each document is scored on multiple signals at once:

Signal 1: technical access

If robots.txt blocks AI crawlers, the site is invisible regardless of content quality. ~35% of RU-language sites still block GPTBot—often a 2023 “just in case” rule never revisited.

Second barrier: JS-only rendering without SSR/SSG—many crawlers see an empty shell.

Signal 2: answer-first structure

Models extract answers to explicit questions. Content that leads with the answer gets cited far more often.

Signal 3: E-E-A-T

Concrete E-E-A-T signals:

• Named author + role + Schema Person
• Clear published and updated dates
• Outbound links to sources and studies
• Third-party authoritative mentions of your brand

Signal 4: semantic density

Models read via semantic triples. Every claim should carry specifics.

Signal 5: Schema.org markup

Markup is a direct vocabulary for LLMs—without it, a FAQ looks like a plain paragraph.

How to earn citations on each AI platform

TL;DR: Use the compact matrix below as a channel checklist on top of the architecture table.

Each platform has a different “source of truth”—fresh search vs classic SEO corpora. Use this table to plan work.

Four prompt types people actually ask models

TL;DR: Informational, recommendational, scenario, and reputational prompts map to articles, roundups, “who it’s for” pages, and sentiment monitoring.

Prompt types map to content priorities. ~70% of LLM prompts are phrased in ways classic keyword tools never captured.

How to write content models actually cite

TL;DR: Write answer-first, neutral tone, tables + FAQ; the Princeton GEO benchmark (KDD 2024) shows quotes and citations beat keyword stuffing.

Rule 1: answer in the first two sentences

Rule 2: headings as questions

Models cite sections whose H2 mirrors the user’s question—a direct hint that an answer lives here.

Rule 3: specifics instead of abstractions

Models favor verifiable facts—numbers, dates, names, outcomes. If you can prove it, say it outright.

Rule: neutral tone, not promotional

Semrush studied 700k+ pages vs AI citations (2025): promotional copy correlated at roughly −26.19% with being cited. That’s not “be nicer”—it’s a structural trust signal.

Models treat adjectives and hype as bias risk—biased sources get skipped when a neutral alternative exists.

Practical test: if an independent journalist wouldn’t say the sentence on air, rewrite it.

Rule 4: FAQ block on key pages

Question → ≤80-word answer is the most citable pattern. Add 5–7 real customer questions and mark them up as FAQPage.

Rule 5: tables over prose

Structured data beats long prose—comparisons, specs, and checklists belong in tables or lists.

What actually moves the needle: Princeton GEO Study

Aggarwal et al. (Princeton / Georgia Tech / IIT Delhi, ACM KDD 2024)—the first academic GEO benchmark—tested nine content tactics across 10k prompts. Ranked lift/drag effects below.

Surprise: adding citations helped URLs sitting in organic position ~5 the most (+115%), while #1 URLs dropped ~30.3%—#1 in Google is not #1 in the generative answer.

Second: keyword stuffing was the only tactic with a negative effect—classic SEO padding actively hurts GEO.

Source: Aggarwal et al., GEO: Generative Engine Optimization, ACM KDD 2024

Compare the takeaways above with live answers for your brand vs competitors:

Off-site signals: why your own site isn’t your best GEO asset

TL;DR: Third-party mentions often outweigh your own domain—vc.ru, Habr, lists, and trade press build the independent consensus models amplify.

Here’s the counter-intuitive takeaway from academic GEO research. Princeton, Georgia Tech, and IIT Delhi (2024) argue models trust third-party sources more than a brand’s own pages. Models synthesize a “reputation consensus”—when five independent outlets agree your tool is the best pick for LLM monitoring, that signal compounds. When it’s only you, it doesn’t.

Conductor’s 2026 GEO benchmarks show brands mentioned on 10+ independent domains get cited in AI answers ~3.2× more often than brands limited to their own site.

Where mentions matter for Russian-language GEO

vc.ru and Habr are indexed with high priority by YandexGPT and GigaChat. A substantive article or case mention on vc.ru often outweighs ten new pages on your domain.

Aggregators and “top 10 tools” roundups on independent domains increase how often you appear in recommendational prompts.

According to GenOptima (2026), ~32.5% of AI citations land on comparative articles.

Trade media—Cossa, Sostav, RB.ru for marketing; TexTerra, Searchengines.ru for SEO—create independent validation signals models treat seriously.

G2, Product Hunt, and Capterra—aggregators with verified reviews—are heavily used by ChatGPT and Perplexity on recommendational prompts.

A practical off-site plan

1. List 15–20 domains where competitors show up and you don’t—validate with Ahrefs or manually.
2. Every month, earn one real mention or article—not a generic press blast.
3. Target 3–5 independent roundups in your niche over three months.
4. Track outcomes in Getllmspy—lift often shows within 2–4 weeks after the page is indexed.

After off-site placements, check whether models started citing you:

How to measure GEO: metrics and tools

TL;DR: Track AI referrals, branded GSC, manual prompts, and LLM-Score together—referrals alone understate impact by ~80–85%.

Measuring GEO is still the biggest gap. According to DOJO AI (2026), only ~16% of brands systematically track their presence in AI answers—that’s your competitive window.

Metric 1: AI referral traffic (measurable)

In GA4, create a segment for chatgpt.com, perplexity.ai, gemini.google.com, ya.ru (YandexGPT search), and claude.ai to baseline referrals.

Context: Google still sends ~345× more traffic than all AI platforms combined (Previsible, Sept 2025). AI referrals are small but reportedly +527% YoY—measure the trend, not the absolute.

Metric 2: branded search lift in GSC (indirect)

A model mentions your brand → the user googles you. In GSC, filter queries containing your brand; growth here alongside flat/falling classic SEO traffic is a GEO signal.

Metric 3: manual prompt testing (free, labor-heavy)

Monthly, run 10–15 target prompts in ChatGPT, Perplexity, and YandexGPT—log mention, list position, and sentiment. Use a simple table:

Metric 4: LLM-Score in Getllmspy (automated)

Automate mention frequency and sentiment across ChatGPT, Perplexity, Gemini, and YandexGPT—reports show Share of Voice dynamics vs competitors.

What not to do

Don’t judge GEO on referrals alone—you’ll see ~15–20% of the real effect. Much of it shows up as branded curiosity and direct traffic, not chatgpt.com clicks.

Automate weekly multi-model checks, competitors, and sentiment with Getllmspy—start from the free check page.

Analyzer: score your copy for GEO readiness

TL;DR: The inline checker scores a paragraph on seven source-selection heuristics—a fast pre-publish sanity test.

Paste any paragraph from your site—seven checks mirror how models shortlist sources.

Inline scoring is directional; the full multi-model view is on the check page:

Three visibility scenarios: sample AI answers

TL;DR: Three scenarios show how answers shift from leader to missing brand to negative positioning.

Prompt Win Rate: before and after GEO

TL;DR: Prompt Win Rate is the share of test prompts where your brand appears in the intended context; the chart benchmarks models from our panel.

Prompt Win Rate = share of prompts where the model mentions your brand.

Test yourself: GEO quiz

TL;DR: Five questions test GEO reflexes: answer-first, robots.txt, Seer correlations, FAQPage, and “#1 in Google ≠ #1 in ChatGPT.”

LLM-Score simulator: gauge your potential

TL;DR: Sliders approximate signal strength for intuition—not a replacement for LLM-Score, but a useful weighting toy.

Step plan: 30 days to LLM visibility

TL;DR: The 30-day plan sequences unblockers, markup, content, off-site proof, then measurement habits.

Copy-ready snippets: robots.txt & Schema.org

TL;DR: Three crawler-facing artifacts: robots.txt allowing AI bots, FAQPage JSON-LD, and llms.txt describing what LLMs should read.

robots.txt — allow AI crawlers

# One group for * and AI bots — Disallow applies to all (REP groups are not merged).
User-agent: *
User-agent: GPTBot
User-agent: OAI-SearchBot
User-agent: anthropic-ai
User-agent: ClaudeBot
Allow: /

Disallow: /admin/
Disallow: /api/

Schema.org FAQPage — JSON-LD

<script type="application/ld+json">
{
  "@context": "https://schema.org",
  "@type": "FAQPage",
  "mainEntity": [
    {
      "@type": "Question",
      "name": "What is GEO optimization?",
      "acceptedAnswer": {
        "@type": "Answer",
        "text": "GEO (Generative Engine Optimization) is optimizing content for generative AI: ChatGPT, Gemini, Perplexity, YandexGPT. The goal is to become a source the model cites in its answer—not just a link in a search list."
      }
    },
    {
      "@type": "Question",
      "name": "How is GEO different from SEO?",
      "acceptedAnswer": {
        "@type": "Answer",
        "text": "SEO targets Google/Yandex crawlers (rankings in link lists). GEO targets language models (brand citations in AI answers). Key GEO signals: E-E-A-T, answer-first structure, Schema.org markup, semantic density."
      }
    }
  ]
}
</script>

llms.txt — navigation for LLM crawlers

# Getllmspy — LLM brand monitoring

## Description
Getllmspy measures brand visibility in AI model responses.
Checks ChatGPT, Gemini, YandexGPT, GigaChat, Perplexity.
Returns LLM-Score (0–100), Share of Voice, competitor analysis.

## Key pages
- /en/check — Free brand check (start here)
- /en/pricing — Pricing plans
- /en/faq — Frequently asked questions
- /en/blog/geo-aeo-optimization — GEO/AEO guide

## Target audience
Marketing managers, SEO specialists, brand managers,
digital agencies managing multiple clients.

Nine mistakes that kill LLM visibility

TL;DR: Nine mistakes span crawler blocks, rendering, authors, dates, promo copy, schema, off-site proof, and measurement—fix top-down by extraction impact.

Five GEO myths that slow you down

TL;DR: We debunk myths like “GEO is SEO with ChatGPT” or “llms.txt is enough” so teams don’t burn quarters on the wrong work.

GEO services are in a noisy hype phase—vendors promise “secret” plays. Here are the common myths and the reality behind them.

GEO by niche: where AI visibility matters most

TL;DR: Prompt mix and surfaces shift by vertical—B2B SaaS, local, ecommerce, and healthcare each get different tactical emphasis.

Not every niche depends on GEO the same way—potential and competition differ. Pick a vertical to see typical prompts and difficulty.

Real patterns: what works in practice

TL;DR: Cases repeat a pattern: access + structure + off-site proof + measurement moves PWR over months, not days.

Next frontier: GEO for AI agents

TL;DR: Agentic search (Operator-class tools) rewards machine-readable pricing, X-vs-Y tables, and parameterized FAQ—ship Product/Offer schema early.

OpenAI’s Operator (Jan 2026) is an agent that doesn’t just answer—it drives the browser to book, compare plans, and fill forms. Google, Perplexity, and Anthropic are shipping similar agent stacks.

Agentic retrieval still cares about relevance, but it overweight machine-readable specs—pricing grids, feature matrices, and step lists agents can scrape without guessing.

What to ship now

• Product + Offer schema: for SaaS, expose price, entitlements, and policies in JSON-LD so agents can quote you without clicking away.
• Comparison hubs (“X vs Y”) with tight spec tables are default agent fuel for recommendational tasks. GenOptima (2026) attributes ~32.5% of AI citations to comparative content.
• FAQPage pairs for parametric intents (“CRM under $50/mo for 10 seats”) give agents extractable constraints.

Further reading: Frase GEO Guide 2026

Glossary: GEO/AEO terms in one place

TL;DR: The glossary aligns the team (and models) on terms like PWR, LLM-Score, and AEO.

FAQ: GEO & AEO

TL;DR: FAQ answers reader objections and mirrors FAQPage markup intent on-page.