LLM optimization (LLM SEO): how to get your brand into AI answers

Which "LLM optimization" is this page about?

This page is about LLM optimization for brands and content: making large language models mention, recommend and link to you. The phrase has a second, older meaning in machine learning, and Google mixes the two. When we pulled the US top 10 for "llm optimization" in Ahrefs, five organic results were engineering pages (NVIDIA, Mirantis, Iguazio, a Medium explainer and OpenAI's own accuracy guide) and three were marketing pages (Semrush, Conductor and an r/SEO thread).

If you came here to make a model faster or cheaper to run, you want the engineering sense, and NVIDIA's guide to inference optimization is a good start.

What is LLM SEO, and how is it different from GEO and AEO?

LLM SEO is the same job as generative engine optimization (GEO) and answer engine optimization (AEO), under a name that points at the model instead of the interface. GEO comes from a 2023 Princeton-led paper10; LLM SEO is the practitioners' term for the same tactics. Our GEO vs AEO vs SEO comparison goes through where the edges actually differ.

Google's position is blunt. Its guide to generative AI features says that from Search's perspective, AEO and GEO are SEO, and that AI Overviews and AI Mode "are rooted in our core Search ranking and quality systems"7 (Google Search Central). For Google's own surfaces, that's largely right. For ChatGPT, Claude and Perplexity, which run their own retrieval and their own crawlers, a good Google ranking is a weaker predictor than most people assume (more on that below).

How do LLMs decide which sources and brands to mention?

An LLM answers from two places: what it absorbed during training (its parametric memory) and pages it retrieves at answer time. The second pattern is retrieval-augmented generation, described in a 2020 paper by Lewis and colleagues that paired a language model with a searchable index of passages8 (arXiv). Every consumer AI assistant now does some version of this when a question needs current or specific facts.

The training path

When a model answers without searching, it draws on training data collected before its knowledge cutoff. Brands that were written about often, and consistently, in the years before that cutoff come out of the model's memory first. You can't edit that memory. You can only add to what the next model learns, by being described in more places, and you can control whether your own pages are part of it through the training crawlers: GPTBot for OpenAI1, ClaudeBot for Anthropic3, and the Google-Extended token for Gemini5.

The retrieval path

When a model searches, it rewrites the question into one or more shorter queries, sends them to a search index, reads a handful of results and writes from them. OpenAI's help center describes exactly this for ChatGPT: it typically rewrites your prompt into targeted queries for third-party search providers and may follow up with narrower ones2 (OpenAI). Google calls its version query fan-out, "issuing multiple related searches across subtopics and data sources"6. Which index gets searched depends on the engine:

Two numbers show how far this is from classic SEO. Ahrefs found that only about 12% of URLs cited by ChatGPT, Gemini, Copilot and Perplexity ranked in Google's top 10 for the same prompt; Perplexity was the closest to Google at 28.6%11 (Ahrefs). Even Google's own AI Overviews drift from its blue links: in a March 2026 study of about 4 million cited URLs, 37.9% also appeared in the top 10 for the query, down from about 76% in Ahrefs' earlier analysis12 (Ahrefs). Fan-out explains most of the gap: the engine is searching sub-questions you never typed, and the pages that answer those sub-questions get cited. Our explainer on how AI engines choose their sources goes engine by engine.

What happens to your page after retrieval

Research on long contexts found that models answer best when the relevant information sits near the start or end of the input and do worse when it's buried in the middle9 (Liu et al.). That study tested question answering, not AI search, so treat this as our reading of it: a page whose answer is stated plainly near the top, in a sentence that makes sense on its own, gives the model less to dig through.

The five levers that move LLM visibility

Everything that reliably moves AI answers falls under one of five levers. They're listed in the order we'd fix them, because each one depends on the one before it.

1. Crawl access: separate search bots from training bots

Most sites that block AI crawlers block the wrong ones. In our study of AI crawler access on 261 major sites, 31 of the 59 sites that disallow GPTBot still allow OAI-SearchBot, which is a deliberate choice to stay out of training while staying in ChatGPT search. Accidental blocks usually sit in the CDN: Cloudflare announced default blocking of AI crawlers on 1 July 202519 (Cloudflare), so a site can say "allow" in robots.txt and still return 403s.

Our default for a business that wants to be recommended: allow every search crawler, and allow training crawlers unless you sell the content itself (news, research, courses). OpenAI says robots.txt changes reach its search systems in about 24 hours1. Here's the starting file:

# AI search crawlers: keep these open if you want to appear in answers
User-agent: OAI-SearchBot
Allow: /

User-agent: Claude-SearchBot
Allow: /

User-agent: PerplexityBot
Allow: /

# Training crawlers: block only if your content is the product
User-agent: GPTBot
Allow: /

User-agent: ClaudeBot
Allow: /

User-agent: Google-Extended
Allow: /

Then check what the bots actually get. On a standard combined log format, this counts status codes per AI user agent for the last log file:

grep -E "OAI-SearchBot|Claude-SearchBot|PerplexityBot|GPTBot|ClaudeBot" access.log \
  | awk '{print $9}' | sort | uniq -c

Done when: every search crawler gets 200 responses on your pricing, product and comparison pages, and no CDN or WAF rule returns 403 or a challenge page to them.

2. Citable passages: answers that survive being lifted out

The GEO paper tested nine rewriting tactics on a 10,000-query benchmark10 (Aggarwal et al., KDD 2024). On its main visibility metric, a baseline of 19.5 rose to 27.8 with quotations added, 25.9 with statistics and 24.9 with cited sources. Keyword stuffing fell to 17.8, worse than doing nothing. The more interesting result is who benefits: citing sources raised visibility 115% for pages ranked fifth and lowered it about 30% for pages ranked first, so these edits help challengers far more than leaders.

A SaaS pricing paragraph, before and after (the product is hypothetical):

Before: Our flexible pricing scales with your business. Whether you're a startup or an enterprise, we have a plan that fits your needs, with powerful features and world-class support included.

After: Ledgerly costs $8 per active user per month on the Team plan and $14 on Business, billed annually, with a 14-day free trial. Business adds multi-entity accounting and NetSuite sync. Companies under 50 users usually pick Team; the break-even for Business is the NetSuite integration, which Team doesn't include.

The second version can be quoted in an answer to "how much does Ledgerly cost," "does Ledgerly integrate with NetSuite" and "Ledgerly Team vs Business." The first can't be quoted for anything. Google says you don't need to break content into tiny pieces for AI7, and you shouldn't: keep the page whole and make each paragraph carry one checkable fact. Run drafts through the citability grader to catch the vague ones.

Done when: each money page opens with a sentence that names the product, the category and who it's for, and every claim on the page has a number, a name or a link behind it.

3. Entity consistency: one version of the facts everywhere

Models learn what you are from every place you're described. When your About page says "revenue intelligence platform," G2 lists you under "sales analytics," and Crunchbase still carries your 2021 tagline, a model has three entity descriptions to choose between and no reason to prefer yours. Write a one-page fact sheet and push it out to every profile you control:

Field            | Value (use word for word)
Category label   | expense management software for mid-size companies
One-line pitch   | Ledgerly automates expenses and cards for 100 to 1,000 employee companies
Founded / HQ     | 2019 / Austin, Texas
Pricing model    | per active user, public pricing page
Key integrations | NetSuite, QuickBooks, Xero
Profiles to sync | About page, LinkedIn, Crunchbase, G2, Capterra, Wikidata (if notable)

Use Organization schema whose sameAs list points to each of those profiles. Google says no special schema is needed for its AI features but asks that structured data match the visible text6, so treat schema as a consistency check. Nobody has shown it works as a ranking trick.

Done when: the category label and core facts read the same on every profile in the list, and the schema matches the page.

4. Third-party mentions: be named where the model reads

This lever usually decides "best X" prompts. In Ahrefs' study of 75,000 brands, branded web mentions correlated with AI Overview visibility at 0.664, branded anchors at 0.527, Domain Rating at 0.326 and backlink count at 0.21813 (Ahrefs). Correlation isn't cause, and big brands have more of everything, but the direction matches what the retrieval path predicts: the model repeats what the pages it reads agree on.

Which pages those are varies by engine and shifts fast. Profound's analysis of 680 million citations found Wikipedia made up 47.9% of ChatGPT's top 10 cited sources and Reddit 46.7% of Perplexity's15 (Profound). Semrush then watched the share of ChatGPT responses citing Reddit fall from close to 60% in early August 2025 to around 10% by mid-September16 (Semrush). So don't build a plan on someone's list of "domains AI loves." Pull the cited URLs for your own prompts and work that list: for each of the top 20, note whether you're named, who owns the page and what would make them add you.

Google warns that seeking inauthentic mentions "isn't as helpful as it might seem"7. Paid placements on low-quality listicles are the obvious case. Earned inclusion in reviews, comparisons and community threads where you actually fit is the version that holds up.

Done when: you're named on at least half of the 20 URLs most often cited for your prompt set.

5. Freshness: update facts, not dates

AI assistants cite newer pages than Google's organic results do. Ahrefs analyzed about 17 million cited URLs and found they averaged 1,064 days since publication against 1,432 for organic results, 25.7% fresher; ChatGPT's citations were the newest, at 458 days newer than organic. Google's AI Overviews were the exception, citing pages about 16 days older14 (Ahrefs).

Put your money pages and most-cited guides on a quarterly review and change something real each time: new prices, a new comparison row, a new data point. Bumping a date with no new content gives a model nothing new to cite.

What's hype in LLM optimization?

Plenty of LLM SEO advice is unproven or contradicted by the engines' own documentation:

How do you measure LLM optimization?

Measure it on a fixed set of prompts, run several times per engine, because single answers vary too much to read. The 12-prompt SparkToro study ran each prompt 60 to 100 times per platform and found that lists almost never repeat, while a brand's appearance rate across many runs held far steadier than its position17 (Search Engine Journal). That gives you the core metrics:

• Mention rate, the share of runs that name you. The headline number.
• Citation rate, the share of runs that link one of your URLs.
• Share of voice, your mentions divided by all brand mentions across the set.
• Accuracy, the share of answers that describe your pricing, category and audience correctly.

First-party data fills in the rest. For Google, Search Console's generative AI performance report counts impressions in AI Overviews and AI Mode, and Google says it reached all sites on 31 August 202620. Bing Webmaster Tools' AI Performance report, in public preview since February 2026, counts how often Copilot and Bing's AI summaries cite your pages and samples the grounding queries behind them21. ChatGPT adds utm_source=chatgpt.com to referral links22, so GA4 shows which pages it sends people to. Clicks will understate the effect: Pew found people clicked a link inside a Google AI summary on about 1% of visits where one appeared18.

Our guide to measuring AI visibility has the prompt-set template and sample-size math.

A six-step LLM optimization plan for the first 60 days

Set expectations by lever. Access fixes can show up within days, given OpenAI's 24-hour figure. Rewritten pages count once they've been recrawled and retrieved, so plan on weeks. Third-party mentions depend on when other people update their pages, so plan on one to three months. Answers that come from training data move only when a new model ships, so don't judge the program on those.

Frequently asked questions

Sources