LLM SEO: How to Get Your Brand Cited in ChatGPT, Perplexity, and Gemini

LLM SEO — large language model search engine optimisation — is the practice of making your content retrievable, interpretable, and trustworthy enough that AI systems choose to cite it when answering user questions.

It's not a replacement for traditional SEO. It's an extension of it into a new retrieval context. Almost everything that makes a page rank well in Google — clear structure, authoritative content, backlinks from trusted sources, technical health — also makes a page more likely to be cited by ChatGPT or Perplexity. But there are important differences in how these systems select content, and those differences create specific optimisation opportunities.

This guide covers both the overlapping fundamentals and the LLM-specific tactics that most guides gloss over.

How large language models find and use your content

Before optimising for LLMs, you need to understand the two distinct pathways through which they surface content.

The training data pathway

LLMs like ChatGPT and Claude are trained on massive datasets scraped from the web. When someone asks a question, the model generates an answer partly from patterns and knowledge absorbed during training. If your brand, product, or content appeared frequently and credibly in training data, the model has an existing "memory" of you.

This pathway is a long-term play. Training data updates happen periodically, not in real time. But it compounds: the more your brand appears across the web in credible contexts — editorial articles, forum discussions, review sites, Wikipedia mentions — the more confidently the model will associate your name with your topic.

The live retrieval pathway (RAG)

Modern AI search tools don't rely solely on training data. When users ask questions, tools like ChatGPT (web search mode), Perplexity, and Google AI Mode perform live web searches to find current information. This process — called retrieval-augmented generation (RAG) — means fresh, indexed content can be cited within days of publication.

Critically, LLMs don't search using your full query. They break it into shorter sub-queries called fan-out queries. A user asking "What's the best project management tool for a remote team of 50 people?" might generate sub-queries like "best project management software 2026", "project management tools remote teams", and "project management software pricing enterprise". Your content needs to rank for these fragments — not just the full conversational question.

ChatGPT's live search runs primarily through Bing. Perplexity uses its own crawler. Google AI Mode uses Google's index. This means Bing Webmaster Tools is no longer optional — it's a distinct citation channel most brands ignore.

How LLM SEO differs from traditional SEO

The foundations are the same. The priorities shift.

Traditional SEO: rank higher in search results, drive clicks to your site, measure success through traffic and position.

LLM SEO: get cited in AI-generated answers, build brand presence in zero-click contexts, measure success through share of voice and citation frequency.

The practical differences:

JavaScript is a liability. Search engine bots can now render JavaScript. Most AI crawlers cannot. Content hidden behind tabs, accordions, client-side rendering, or interactive elements is effectively invisible to LLM retrieval systems. Static HTML is the safe baseline.

Bing matters as much as Google for ChatGPT. ChatGPT's live search runs on Bing. If you only optimise for Google and have never submitted your sitemap to Bing Webmaster Tools, you're missing a primary citation channel for the most widely used AI tool.

Recency bias is stronger. From observed citation patterns, content older than three months sees a measurable drop in AI citations. Quarterly content refreshes matter more for LLM visibility than for traditional rankings.

Brand mentions outweigh links. LLMs give weight to unlinked brand mentions across the web — forum discussions, editorial references, community posts. Being talked about in real community contexts carries substantial signal.

Content structure for extraction, not just for readers. LLMs pull passages, not pages. A paragraph that reads well in context but requires surrounding content to make sense is not extractable. A paragraph that answers a specific question in two sentences is.

Core LLM SEO strategies

1. Make your content accessible to AI crawlers

This is the most commonly missed step. Before anything else works, AI systems need to be able to read your content.

Check your robots.txt for blocks on known AI crawlers: OAI-SearchBot and ChatGPT-User (OpenAI), PerplexityBot (Perplexity), Google-Extended (Gemini), ClaudeBot (Anthropic). Cloudflare recently began blocking AI bots by default — if you use Cloudflare, verify your settings.

Use server-side rendering or static site generation. Avoid putting critical content inside JavaScript-rendered elements. Keep important information in the raw HTML the server returns.

2. Set up Bing Webmaster Tools

Straightforward but consistently overlooked. Go to Bing Webmaster Tools, verify your site, and submit your sitemap. This directly affects ChatGPT citations — there is consistent evidence that Bing rankings translate to ChatGPT search inclusion.

Submit your sitemap to both Google Search Console and Bing Webmaster Tools. Many sites only do Google. This is leaving a major retrieval channel unoptimised.

3. Structure content for extraction

LLMs extract passages, not pages. Make each section self-contained and directly answerable.

Use descriptive headings that mirror how people actually ask questions. "How do I reduce churn?" performs better than "Churn Management". Each H2 should describe exactly what follows.

Write the answer first, then provide supporting detail. Don't make the AI (or the reader) wade through three paragraphs of context to reach the useful sentence. Front-loaded answers are extractable answers.

Use lists and tables where appropriate. Structured formats are easier for LLMs to parse and repackage. Keep paragraphs short and focused on one idea.

4. Implement schema markup

Every page that gets cited in ChatGPT search results has schema markup. Not as a direct ranking factor — as a signal of content quality and structure that correlates strongly with citation.

Validate your schema at Schema.org's validator and Google's Rich Results Test before deploying.

5. Write original, human-authored content

This is counterintuitive to state but essential. AI systems are trained on new information. Content that an LLM generated is not new information — it's a recombination of patterns already present in training data. LLMs trained on AI-generated content degrade over time (model collapse). The models themselves prioritize genuinely novel information.

Original research, first-party data, proprietary frameworks, expert case studies, and real-world examples are disproportionately cited. Generic how-to content that recombines existing information is not.

The test: could a competitor replicate this content tomorrow without access to your specific knowledge or data? If yes, it probably won't be prioritized by AI systems seeking novel information.

6. Optimize for fan-out queries, not just the long question

Identify the sub-queries an AI system would generate from a user's full question. These tend to be 3-6 word phrases covering specific aspects of the broader topic.

Use ChatGPT's autocomplete in an incognito window: start typing about your topic and see what completions appear. These reflect real query patterns. Ask ChatGPT itself to generate likely sub-queries for a question: "What shorter queries would an AI search system generate when answering [question]?" This is free research into how retrieval systems decompose questions.

Make sure your content clearly addresses each sub-query, not just the composite question.

7. Keep content fresh

Refresh important pages at least quarterly. Update statistics, replace outdated examples, add recent developments. Critically: update your Article schema's `dateModified` field to reflect the actual change date. LLMs detect recency signals through both publish dates and freshness of information.

This applies especially to "best of" lists, comparison content, and any pages covering rapidly changing topics. A list updated in Q1 will outperform an identical list from the previous year, all else equal.

8. Build community presence — the most overlooked factor

Here's what most LLM SEO guides treat as a footnote but deserves to be treated as a primary strategy.

For product recommendation queries — "what's the best tool for X", "alternatives to Y", "what do people actually use for Z" — LLMs weight community discussion heavily. Reddit appears in 11% of ChatGPT citations (Profound's 30M citation study) and 46.7% of Perplexity citations. Hacker News threads, LinkedIn discussions, and industry forum posts all feed the retrieval pool.

When a VP of Engineering posts on LinkedIn "thinking of switching from [competitor], what are people using?" and your product is mentioned helpfully in that thread, that's a community citation signal that feeds LLM retrieval. When someone asks on r/SaaS "what's the best CRM for a 10-person startup?" and your product appears in a well-upvoted comment, that thread has LLM citation potential.

The mechanism: authentic, upvoted community discussions about your product category accumulate in the citation pool that LLMs draw from when answering product and category questions. This is distinct from your website content, and it's the channel most brands are systematically ignoring.

Building community citation signal manually: Monitor relevant Reddit subreddits, LinkedIn Groups, Hacker News threads, and industry forums for conversations where your product is relevant. Participate authentically — answer questions, contribute to comparisons, be present in recommendation discussions.

Building community citation signal systematically: Tools like Handshake monitor Reddit, LinkedIn, X, Hacker News, Facebook Groups, and industry forums simultaneously for buying intent conversations — recommendation requests, competitor comparisons, problem discussions where your product is relevant. When a relevant conversation appears, Handshake drafts a contextually appropriate reply and posts it from your account, building consistent community presence at a scale that manual monitoring can't sustain.

The compounding effect: every authentic, helpful mention of your product in a relevant community discussion is a potential future LLM citation. Brands that build this community presence systematically show up in AI answers to product questions. Brands that don't are invisible to the retrieval pool that matters most for buying decisions.

Measuring LLM SEO performance

Unlike traditional SEO, there's no dashboard showing LLM "rankings." Measurement requires a combination of approaches:

Manual testing: Enter 20-30 relevant queries into ChatGPT, Perplexity, and Gemini monthly. Note whether your brand is cited, in what context, with what sentiment, and which competitors appear. Log in a spreadsheet. This is time-consuming but gives unfiltered signal.

Share of voice tools: Semrush AI Visibility Toolkit, Otterly.AI, Nightwatch, and Profound all track brand mention frequency and share of voice across AI platforms. These range from $29/month (Otterly) to enterprise pricing (Profound). The most important metric is share of voice across relevant queries — how often your brand appears relative to competitors for category questions.

Referral traffic from AI platforms: Set up custom GA4 tracking for known AI referral domains: chat.openai.com, perplexity.ai, gemini.google.com, claude.ai, copilot.microsoft.com. This traffic tends to be high-intent — users who've already received information about your brand and are actively choosing to visit. Track separately from organic.

Bing Webmaster Tools: Monitor your Bing rankings for priority queries. Given ChatGPT's reliance on Bing, Bing position correlates meaningfully with ChatGPT citation frequency.

Common LLM SEO mistakes

Blocking AI crawlers. The single most common issue. Check robots.txt and CDN settings before any content work.

Publishing AI-generated content. Counterproductive for AI visibility. LLMs want new information; recycled AI output isn't new information.

Ignoring Bing. ChatGPT searches Bing. If you haven't set up Bing Webmaster Tools and optimised for Bing rankings, you're under-invested in your largest AI citation channel.

Hiding content in JavaScript. Tabs, accordions, interactive elements — if AI crawlers can't read it, it doesn't exist.

Letting content go stale. Quarterly refreshes are now maintenance, not optional.

Optimising only your own site. Training data and retrieval both draw from third-party sources. Community mentions, editorial references, forum discussions, and review sites all feed LLM visibility. Your own site is one input.

Treating LLM SEO and traditional SEO as separate strategies. They're reinforcing. Strong Google rankings feed into ChatGPT's Bing-powered live search. Good schema markup helps both. Fresh, structured, expert content serves both audiences.

For implementation context, review Google Search documentation.