ChatGPT & LLMs
LLM SEO: How to Get Your Brand Into LLM Answers in 2026
LLM SEO explained: how large language models learn about brands, how LLM search optimization works per engine, and which LLM SEO tools help you measure it.
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LLM SEO is the work of getting your brand mentioned, and your pages cited, when people ask large language models like ChatGPT, Claude, Gemini and Perplexity for recommendations or answers. It has two parts: influencing what a model already knows from its training data, and ranking in the web results the model retrieves when it searches live. The second part is where most short-term gains come from, and it rests on normal SEO foundations plus a few LLM-specific changes.
This guide explains how LLMs come to mention a brand, what LLM search optimization looks like per engine, and which LLM SEO tools are worth using.
How do LLMs decide which brands to mention?
A large language model can "know" about you in two ways, and they call for different work.
Parametric knowledge (training data). During training, the model reads a large snapshot of text, much of it from the public web. Facts that appear often and consistently across many sources are more likely to be learned. That knowledge is frozen at the model's training cutoff. If your brand launched after the cutoff, or was rarely discussed before it, the model may not know you at all without searching.
Retrieved knowledge (live search). When an assistant searches the web to answer, it runs queries, pulls a handful of pages and writes from them. Here the model can mention a brand it has never seen in training, as long as the retrieved pages mention it. This is where the answer usually changes fastest after you publish or earn coverage.
In practice, most commercial prompts ("best project management tool for agencies", "X vs Y") trigger search in the major assistants, so retrieval dominates. But the model's prior knowledge still colours which brands it trusts and how it describes them.
LLM SEO vs traditional SEO
LLM SEO keeps most of SEO and changes the finish line.
| Traditional SEO | LLM SEO | |
|---|---|---|
| Goal | Rank a URL | Be named and cited in the answer |
| Crawlers that matter | Googlebot, Bingbot | Plus OAI-SearchBot, Claude-SearchBot, PerplexityBot and training bots |
| Content unit | Page | Passage that answers one sub-question |
| Off-site work | Links | Mentions on sources the model retrieves and trusts |
| Freshness | Recrawl and re-rank | Recrawl for search; next model version for training data |
| Metric | Position, clicks | Mention rate, share of voice, citations, accuracy |
The overlap is large enough that Google describes optimizing for its own generative AI features as still SEO. The differences are in which bots you allow, how you write passages and what you measure. For the broader framing, see GEO vs SEO and AEO vs SEO.
LLM search optimization, engine by engine
Each assistant retrieves differently. The first job is making sure the right bot can reach you.
| Assistant | Search bot to allow | Training control | Official documentation |
|---|---|---|---|
| ChatGPT | OAI-SearchBot (and ChatGPT-User for user-requested pages) | GPTBot | OpenAI crawlers |
| Claude | Claude-SearchBot (and Claude-User) | ClaudeBot | Anthropic crawlers |
| Perplexity | PerplexityBot (and Perplexity-User) | Not used for foundation models, per Perplexity | Perplexity bots |
| Google AI Overviews, AI Mode | Googlebot | Google-Extended (Gemini training and grounding) | Google AI features |
| Microsoft Copilot | Bingbot | Not covered here | Bing AI Performance |
A few specifics worth knowing:
- ChatGPT. OpenAI documents OAI-SearchBot as the bot that surfaces websites in ChatGPT's search features, separate from GPTBot for training. Blocking GPTBot alone does not remove you from ChatGPT search. More detail in our ChatGPT SEO guide.
- Claude. Anthropic says blocking Claude-SearchBot or Claude-User may reduce your visibility in Claude's answers.
- Perplexity. Perplexity says PerplexityBot surfaces and links websites in its results and is not used to crawl content for AI foundation models, so allowing it has no training trade-off. See how to rank in Perplexity.
- Google. AI Overviews and AI Mode use the normal index and the normal snippet controls. Google says there are no additional technical requirements. Blocking Google-Extended does not remove you from them.
- All of them. Vercel's December 2024 analysis found the major AI crawlers it observed did not render JavaScript. Server-render anything you want an LLM to read.
LLM SEO tactics that work
Make your brand an unambiguous entity
Models mix up brands with similar names and repeat outdated facts. Use one consistent name, one-line description and category everywhere: your homepage, About page, social profiles, directories and marketplace listings. Add Organization structured data that matches the visible page. State plainly what you are, who it is for and what it costs. If the first paragraph of your About page could describe any company, a model has nothing specific to learn.
Write passages that answer one question each
Retrieval systems pull passages, and models quote self-contained statements. Put the answer in the first sentence under each heading, then explain. Use the terms people use in prompts, not internal jargon. Google's AI optimization guide is clear you don't need to chop content into tiny pieces or write in a special way for AI, so this is about clarity, not a new format. Our checklist for optimizing content for AI search goes step by step.
Add verifiable specifics
The GEO paper (KDD 2024) found that adding quotations, statistics and cited sources to content improved its visibility in generated answers by roughly 30 to 40% on the paper's main metric, while keyword stuffing did little. Specific numbers, dates, named sources and first-hand data are what make a passage worth using.
Get mentioned where models read
For both training and retrieval, repetition across independent sources matters. A brand that appears in comparison articles, review sites, industry publications, community threads and documentation is more likely to be learned and retrieved than one that only describes itself. Start with the domains that assistants already cite for your prompts: that is a concrete outreach list. Avoid paid or fake mentions; Google's guide notes that seeking inauthentic mentions isn't as helpful as it might seem.
Decide your training-crawler policy on purpose
Allowing training crawlers (GPTBot, ClaudeBot, Google-Extended, Applebot-Extended, CCBot) means your public content may inform future models. Blocking them protects content but may leave future models knowing less about you when they answer without search. There is no universal right answer. What is almost always a mistake is blocking search bots by accident with a blanket "block all AI" rule. Our AI crawlers reference has robots.txt templates for each policy.
Keep facts fresh across the web
When a model states a wrong price or a feature you retired, the cause is usually a stale page somewhere: an old blog post, a directory listing, a partner page. Audit what the top-cited pages say about you and get them updated. Put visible "updated" dates on pages with changing facts.
LLM SEO tools: what to use for each job
LLM SEO tools and LLM optimization tools fall into four groups. You rarely need all four from one vendor.
1. Visibility trackers. These run prompts across assistants and record mentions, position, citations and sentiment. As of September 2026, the main options include Profound (enterprise, 9+ engines, prompt-volume data), Peec AI (mid-market), Otterly.AI (from $29 per month for 15 prompts, with some engines as add-ons), LLMrefs (free plan, $79 per month for 11 engines), Promptwatch, Gauge, AthenaHQ, Scrunch and SE Ranking's AI tracking. aeotime is our product: it tracks ChatGPT, Perplexity, Gemini, Claude, AI Overviews, AI Mode, Grok and DeepSeek from $99 per month for 250 prompts. Our best AI visibility tools comparison covers each in detail.
2. Crawler and access checkers. An LLM SEO checker in this group tests whether AI user agents can reach your pages through robots.txt and your firewall. Use the AI crawler checker and the crawler view tool to see what non-rendering bots receive.
3. Content and structure tools. These check whether pages are easy to extract from: heading structure, direct answers, schema. Free options include the AEO checker, heading analyzer, schema validator and llms.txt generator.
4. Platform reports. Google Search Console has a generative AI performance report for Google's AI features, and Bing Webmaster Tools has an AI Performance report showing citations in Copilot and Bing's AI summaries. They are free and first-party, but each only covers its own engine.
When choosing a tracker, check three things: which engines are included without add-ons, how many prompts you get, and whether answers are collected through official APIs or browser sessions. API-collected answers can differ slightly from what a logged-in user sees, so the value is in consistent trends, not exact screenshots.
How to measure LLM SEO
Pick 50 to 150 prompts that match real buying questions. Run them weekly across the assistants your audience uses and track:
- Mention rate: share of prompts where you are named.
- Share of voice: your mentions against named competitors.
- Citations: which of your URLs, and which third-party URLs about you, are linked.
- Accuracy: whether prices, features and positioning are correct.
Expect noise. The same prompt can return different brands on different runs, so judge four- to six-week trends. Our LLM visibility page shows how aeotime reports these metrics per engine.
Your LLM SEO starting checklist
- Confirm OAI-SearchBot, Claude-SearchBot, PerplexityBot, Googlebot and Bingbot can fetch your key pages.
- Check those pages render their main content without JavaScript.
- Rewrite your About page and homepage opening so a model can state what you are in one sentence.
- Add sourced numbers and a direct answer under every H2 on your top five commercial pages.
- List the domains assistants cite for your prompts and start earning real mentions there.
- Baseline your visibility with the free AI visibility checker, then track weekly.
Frequently asked questions
What is LLM SEO?
LLM SEO is the practice of making a brand, product or page more likely to be mentioned and cited by large language model assistants such as ChatGPT, Claude, Gemini and Perplexity. It covers two routes: what models learn during training, and what they retrieve from the web when they answer with search.
Is LLM SEO the same as GEO or AEO?
They describe the same goal from different angles. GEO (generative engine optimization) and AEO (answer engine optimization) are the more common industry terms. LLM SEO emphasizes the model itself, including training data, while GEO and AEO usually focus on AI search answers. The tactics overlap almost entirely.
Should I block AI training crawlers if I care about LLM SEO?
It is a trade-off. Blocking training crawlers such as GPTBot or ClaudeBot keeps future content out of those companies' training data, which may reduce what future models know about you without a search step. It does not affect AI search results, which use separate search bots. Many brands allow search bots and decide on training bots based on how much they rely on original content for revenue.
What is the best LLM SEO tool?
It depends on what you need. For tracking mentions and citations across engines, AI visibility tools such as Profound, Peec AI, Otterly.AI, LLMrefs and aeotime are the main category. For technical checks, crawler checkers and rendering tools matter more. Pick based on the engines you need and your prompt count.