ChatGPT has 900 million weekly active users, OpenAI said in February 2026. Many of them ask one long question where they once typed keywords into Google. That shift is why LLM SEO matters.
LLM SEO is search engine optimization for a large language model, the system behind ChatGPT, Perplexity and Gemini. The prize is a recommendation, which means the model names your brand when a buyer asks about your niche. This post gives seven steps, and each figure names its source.
TLDR
Buyers type a full question, so write the page that answers the full question.
Ask ChatGPT your buyer's question first, then log the brands and sources it names.
Open each key page with a two-sentence answer and one statistic with its source named.
In a Princeton and IIT Delhi study, quotes, statistics and cited sources lifted visibility 27% to 41%. Keyword stuffing lowered it.
Track the same ten prompts every week, because 91% of cited URLs appear in only one of three engines.
A Page Built for the Prompt Gives the Model Something to Quote
Take one question a buyer types into ChatGPT. It reads: "What's the best CRM for a small sales team that needs easy automation and integrates with Slack?" Compare two pages that could answer it.
Page A targets the keyword "best CRM". It opens with company history and never answers the Slack question.
A model has nothing on Page A to quote. The answer names two rivals whose pages do.
Page B uses the buyer's full question as its title and opens with a two-sentence answer.
Page B adds one statistic with its source named, which gives the model a passage to quote.
Whether the model then names you is the measurement in Step 7.
Watch out. This scenario is an illustration. Step 1 gives you the real version, run on your own buyer's question.
A Buyer's Full Prompt Carries Three Constraints a Keyword Drops
Old SEO targeted a keyword such as "best CRM". The prompt above runs 17 words and carries three constraints: a team size, a need and an integration. A page has to match all three to be a candidate for the answer.
Keep your traditional SEO running. Google still handles quick, direct searches and local queries such as "coffee near me". Long research questions and product comparisons are where buyers now open a chat instead.
Step 1. Ask ChatGPT Your Buyer's Full Question
Open a fresh ChatGPT chat and type your buyer's question the way they would say it in Slack. Copy every brand the answer names and every source it lists.
Repeat it for five questions. The brands that appear in several answers are your competitors inside AI answers, whatever your sales team lists. The gap in box 4 becomes your first page edit.
Tip. Start a fresh chat for every question, so earlier messages do not shape the answer.
Step 2. Replace Keywords With Full Prompts
Write your target list as full questions. Find them in five places:
Support tickets and sales calls, where customers use their own words.
Niche forums such as Reddit and industry groups, where the same questions recur.
Your SEO tool, filtered for long-tail question keywords.
Comparisons such as "X versus Y for [use case]" and "cheapest alternative to Z".
The models themselves, when you ask about your competitors and note which prompts open a real discussion.
Signals Proposal is a skill I built for the second item. It reads public Reddit and LinkedIn conversations about a market. It returns ranked actions, content ideas and keywords, each with its reason.
For a free route into the same conversations, read how to do social listening for leads.
Step 3. Answer the Prompt in Two Sentences
Give each prompt its own page. Use the question as the title, and answer it in the first two sentences. Put the explainer first and the comparison page second.
AccuKnox, where I work, tracks 649 prompts in ChatGPT with LLM Labs. From November 2025 to March 2026, its explainer pages earned more citations than its listicles, comparison pages and service pages combined. That is one company's count in one category, so test your own.
An explainer teaches the concept the buyer asked about. A comparison page covers features and pricing, with real pros and cons for each tool. It earns its place once the explainer exists.
Example. Title: the buyer's full question. Sentence 1 names the pick and the team it fits. Sentence 2 gives the one reason, with a cited number.
I apply the same rule to my own site. The first of ten habits I wrote down is a plain-text twin of every page.
Make the page readable as plain HTML before you polish the copy. A page that renders only in the browser can arrive empty. That is the fault behind a vibe coded app with zero SEO. Docs face the same test, and DevRel Uni Cohort 7 shows how to run a quickstart as a coding agent to find the steps that fail.
When the draft is done, grade it. SEO Blog Grader takes a draft or a live URL and returns six scores with a ranked list of fixes.
Step 4. Add One Cited Statistic to Each Key Page
Researchers at Princeton and IIT Delhi tested nine content changes in a paper called GEO: Generative Engine Optimization, presented at KDD 2024. They built GEO-bench, a 10,000-query benchmark, and ran the changes on its 1,000-query test split.
Quotation Addition. It gave the largest gain, 41% on the paper's main visibility metric.
Statistics Addition. It gave 31%.
Cite Sources. It gave 27%, and it lifted a page ranked fifth on Google by 115%.
Keyword stuffing. It lowered visibility by about 8%.
Cite Sources also lowered the top-ranked page's visibility by 30% on average, so the trick favors the smaller page. Combinations helped too. On a 200-query subset, pairing fluency edits with statistics beat every single method by more than 5.5%.
The test engine was built on gpt-3.5-turbo with sources from Google's top five results, and the authors repeated it on Perplexity.ai. It is not live ChatGPT, so read the percentages as direction and confirm them in Step 7.
Rule of thumb. Put one statistic on each key page, with its source named in the same sentence. Add a named expert's quote where a statistic does not fit.
Step 5. Make Every Page Agree With Every Other Page
A model reads your homepage, an old directory listing and a three-year-old blog post. If they name three different customers, the model has no single answer to give.
AccuKnox hit this in a winter 2025 homepage review. The CEO said buyers could not tell what the company sold. The fix was one fixed order for 12 platform modules. That order now drives the homepage, the menu, the sales decks and the collateral.
The AccuKnox case study carries the full story and the CEO's words.
Run three habits to keep pages in agreement:
Audit. Compare the core message on your site, social profiles, directory listings and old posts.
Update. Change pricing and features on every surface in one pass, not only the homepage.
Focus. Name one niche, because a page that serves everyone gives a model no reason to pick it.
Step 6. Earn Mentions Where Each Model Looks
Models read the whole web, so the work continues off your site. In March 2026, Peec AI analyzed 30 million sources cited in answers across five engines in the United States. Peec sells AI search tracking, so read it as vendor research. Reddit ranked first or second on every engine.
The rest of each list differs:
ChatGPT. Wikipedia, Reddit and Forbes lead its list.
Perplexity. G2 appears in its top five and in no other engine's, which Peec flags for B2B.
Google AI Mode and AI Overviews. YouTube ranks first and Facebook third.
Pick the engine your buyers use and work its list. Kevin Indig reported Omnia data on how little the engines overlap. In his May 2026 analysis, 91.07% of cited URLs appeared in only one of ChatGPT, Perplexity and Google AI Overviews. Only 2.37% appeared in all three.
Earn the mentions that carry weight:
Collect real reviews on sites such as G2 and Capterra.
Answer real questions in the forums where buyers gather.
Write guest posts and earn features in industry news.
Correct the auto-generated transcript on each YouTube video before it goes live. Peec saw AI Mode quote both descriptions and transcripts.
For a DevRel view of the same job, read Building AI-First DevRel.
One agency, Margatix, says it moved a partner from 4% to 80% visibility in 90 days with no new content. It describes fixing the entity, mapping cited sources and earning third-party mentions. The thread attaches a visibility chart but no prompt list, so treat it as one agency's claim.
Step 7. Track the Same Prompts Every Week
Pick ten prompts from Step 2 and never reword them. Ask each one three times in each engine your buyers use, because answers vary between runs. Log whether your brand appears and which sources the answer shows.
Change one page per week. A second edit in the same week hides which change moved the answer. A tool such as LLM Labs automates the asking and the logging once the list passes a few dozen prompts.
Five Questions a Marketer Asks About LLM SEO
Is Google dead? No. Keep your traditional SEO running, because Google still handles quick, direct and local searches. LLM SEO adds a second channel for long research questions.
Will a statistic make ChatGPT recommend me? The GEO paper tested a research engine and Perplexity.ai, not live ChatGPT. Use the 31% as direction and confirm it with your own weekly prompts.
Should I still write comparison pages? Yes, after the explainer. In AccuKnox's count, explainer pages out-cited comparison pages, so build them in that order.
How many prompts should I track? Start with ten. AccuKnox tracks 649 across several product categories, which is the scale a larger team reaches.
Why do ChatGPT and Perplexity give different answers? They cite different pages. Indig's analysis found that 91.07% of cited URLs appeared in only one of three engines.
The Appendix Holds the Prompt Log and the Answer Template
A weekly log, one row per run, in a spreadsheet or a CSV file:
week,engine,prompt,run,brand_named,sources_shown
YYYY-MM-DD,ChatGPT,"<buyer question>",1,yes|no,"<url> <url>"The two-sentence answer that opens each key page:
Title: <the buyer's full question>
Sentence 1: <Your pick> fits <the team> that needs <the need>.
Sentence 2: <One reason>, with <one statistic> (<source name, year>).
This feels like mobile optimization in 2010. Ignoring it now is choosing invisibility.
Here's the TL;DR version of this:
• Discovery has shifted from search engines to LLMs.
• AI recommendations rely on distributed consensus.
• Keywords alone no longer define visibility.
• Authority emerges from cross-platform consistency.
• LLM SEO is already a requirement.