Ask ChatGPT a question your buyers ask, and watch a competitor's page get cited instead of yours. That stings. Learning how to get cited in ChatGPT is less mysterious than it looks, because most of the levers sit on your own site or in places you can influence. This guide gives content and marketing teams a workflow to run in order, from crawler settings to authority building.
Here is the short version. ChatGPT cites you when your page is retrievable through its live search path and structured so the answer is easy to lift out. Authority, formatting, and off-site presence all exist to serve those two conditions.
Ready? One step at a time.
Step 1: Map the two paths ChatGPT uses to source an answer
ChatGPT answers from two different places, and they behave nothing alike.
The first is baked-in model knowledge. The base models were trained on large snapshots of public web data, licensed corpora, and curated sources up to a training cutoff. When ChatGPT answers from that memory, it does not browse, it does not retrieve, and it gives you no clickable link. It simply knows things.
The second is live retrieval. When someone turns on web search, or the model decides a question needs fresh information, ChatGPT rewrites the prompt into a search query, pulls results from Bing's index, filters them, and writes an answer with inline citations. Those linked sources you see in the interface come from this path.
Each path asks something different of you.
- To be cited with a link today, you need to be retrievable and citable through Bing's index.
- To become part of what the model already knows, you need presence on the high-trust sources a future training run will ingest: Wikipedia, major publications, review aggregators, Reddit threads, authoritative blogs.
Every attempt to get brand into ChatGPT answers runs through one of those two doors. That framing is the whole of ChatGPT AEO, and it saves you from spending a quarter pushing on the wrong one.
Most of this guide works the live retrieval path, for one honest reason: it is the path you can influence this month. Training data is a long game you start now and collect on later.
You know this step is done when you can say out loud which path you are targeting for each of your priority questions.
Where people go wrong: treating the two as one job. You cannot rank in ChatGPT the way you rank on Google, and you cannot edit the base model's memory on your schedule. Different doors, different keys, different timelines.
Step 2: Make sure ChatGPT is allowed to fetch you
This is the cheapest step and the one that quietly disqualifies the most sites.
OpenAI runs three documented crawlers, each with its own job.
- OAI-SearchBot surfaces websites inside ChatGPT's search results. This is the crawler that fetches pages for citation in live answers.
- GPTBot collects content used to train future foundation models.
- ChatGPT-User fires when a user action sends ChatGPT to fetch a page on demand.
So the rules are simple. To be cited in ChatGPT answers today, allow OAI-SearchBot and ChatGPT-User. Block GPTBot only if you want to opt out of future training data collection, which does not affect whether you can be cited right now. Block all three and you are fully out.
One more line to check: Bingbot. Live retrieval runs on Bing's index, so blocking Bingbot closes the live path entirely, no matter how your OpenAI rules are written.
Common mistake: blocking every bot with "AI" in the name during a nervous week, then wondering six months later why ChatGPT never cites the site. Read your robots.txt line by line before you assume it is fine. Check your CDN or firewall rules too, since those can block crawlers your robots.txt happily allows.
You know this step is done when you have confirmed, not assumed, that all three OpenAI crawlers and Bingbot get a response from your priority pages.
While you are in there, you will probably hear about llms.txt. It is a community proposal for a Markdown file at your site root that lists your key resources in a structure built for language models. It is not an official standard, and no major AI assistant confirms using it as a signal today. Treat it as cheap insurance and forward compatibility, not as a lever. It takes an afternoon and it is not a substitute for robots.txt.
Step 3: Earn a place in Bing's index, then its first page
Here is the part most teams skip. If you do not rank on Bing for a query, you cannot appear in ChatGPT's live citations for it. Pages sitting on Bing's first page are dramatically more likely to be cited than pages buried deeper.
That makes plain technical hygiene a citation strategy:
- Clean, crawlable HTML with the critical content rendered server-side.
- Fast first contentful paint, targeting under about 1.5 seconds.
- Low cumulative layout shift.
- Descriptive URLs with the question or topic in the slug.
- An XML sitemap that stays current.
Slugs matter more than they sound. A generic path like /blog/post-123 underperforms a descriptive one, because slug clarity helps the selector understand what the page is about before it reads a word.
Ranking gets you considered, not cited. You do not rank in ChatGPT so much as get selected by it, and selection happens after retrieval. ChatGPT filters what came back and favors pages where the answer is obviously extractable: a direct definition near the top, a clean list, a table, a tight question and answer block. Plenty of pages rank and still lose here, which is exactly what step 4 fixes.
You know this step is done when your priority pages are indexed on Bing and you know roughly where they sit for your target questions.
Where people go wrong: pouring a year into Google positions while never once checking Bing. Google ranking does correlate with citations, so the work is not wasted. It is just incomplete, and ChatGPT search optimization starts at the index you were ignoring.
Step 4: Write the answer where the extractor reads first
Now the writing itself. ChatGPT's extractors read top-down, and the data shows it clearly. Roughly 44 to 47 percent of citations come from the top third of a page, even though that top third holds only about 37 percent of a typical page's words.
So front-load. Put a direct 40 to 60 word answer to the page's core question inside the first 100 to 200 words. Not a preamble, not a scene-setting paragraph. The answer.
Then structure the body so each part can stand alone:
- Keep section blocks between 120 and 180 words. Pages built that way earn about 70 percent more citations than pages chopped into sections under 50 words.
- Frame H2s as natural-language questions your buyers actually ask.
- Use bullets, numbered lists, comparison tables, and definition blocks where they genuinely fit.
- Add an FAQ block near the end with a handful of question and answer pairs.
- Add an author byline with real credentials, plus visible published and last-updated dates.
Length still counts. Pages under about 800 words average fewer citations than pages over 2,900 words, because thin pages rarely contain a self-contained answer worth lifting.
Format counts too, and some shapes are simply over-represented in citation data. Statistics roundups framed as "X statistics about Y" do well. So do listicles, comparison tables, glossary-style definitions, and clear product pages. If you have original numbers nobody else has, that page is a citation magnet waiting to be written.
Pro tip: do not stuff keywords into the answer block. Forced phrasing makes a passage harder to extract cleanly, which is the opposite of what you want.
What about schema? A controlled study tracked 1,885 pages that added JSON-LD markup against roughly 4,000 matched control pages. ChatGPT citations moved 2.2 percent, a change not statistically distinguishable from zero. Add schema if it is already part of your publishing workflow. Do not treat it as the lever that changes your results.
This is where the writing pipeline does real work. Answer-first structure, heading hierarchy, keyword coverage, internal linking, schema, and metadata are native to how DeepSmith produces an article, so citation-ready formatting is built during creation instead of patched in during review.
You know this step is done when a stranger could skim your first 200 words and repeat your answer correctly.
Step 5: Build the entity authority ChatGPT already leans on
ChatGPT's source selector rewards the same authority signals search engines do, and the gaps are wide. Sites with more than 32,000 referring domains are roughly 3.5 times more likely to be cited than sites under 200. Domains with trust scores above 90 earn around four times the citations of domains below 43. Pages ranking in the top Google positions pull about 60 percent more citations than pages far down the list.
That sounds discouraging if you are a smaller brand. It is not, because you can borrow authority instead of building all of it yourself.
Look at where ChatGPT actually gets its sources. Wikipedia accounts for roughly 48 percent of its top-cited sources. Reddit sits second at about 11 percent. For commercial questions, the model leans on review aggregators like G2, Capterra, GetApp, and Software Advice, plus major publications.
So the off-site work has a clear order:
- Wikipedia. The notability bar is real, and most brands will not earn a standalone page. Aim instead to be cited inside an existing article in your category, and pitch the third-party coverage that could become a citable reference. Keep the tone neutral and follow the conflict-of-interest rules.
- Reddit. Pick three to five subreddits where your buyers ask questions. Build credibility before you mention your brand at all. Answer thoroughly, link only when the link genuinely adds something.
- Earned media. Trade publications, analyst reports, industry awards, guest posts on adjacent high-authority blogs, and inclusion in the roundups your category already reads.
- Review sites. Active review generation and real responses improve both entity authority and surface visibility for buying questions.
There is a quieter signal underneath all four. Being named alongside known entities in trusted contexts, in a Wikipedia reference, an analyst note, a category roundup, builds the association the model carries into its next answer. Co-citation is how a smaller brand starts reading as a real option in its category.
Your mix will differ by category, so check before you commit. Health and finance questions route heavily to government and academic sources. Consumer product questions route to Reddit and review sites. B2B software questions route to review aggregators and analyst reports.
Where people go wrong: one promotional Reddit post from a brand-new account. Communities flag it, it gets downvoted, and you have spent credibility you did not have yet. Authentic participation over months is what compounds.
Step 6: Map the prompts your buyers actually ask
You cannot optimize for questions you have never written down. This is the step that turns ChatGPT search optimization from a vibe into a plan.
Build a working list of the real questions, not a wish list. Pull from four buckets:
- Branded questions, where someone names you directly.
- Unbranded category questions, where they describe the problem instead.
- Comparison questions, the "X vs Y" shape.
- Recommendation questions, the "best tool for X" shape.
Then run each one in ChatGPT with search enabled, and note who gets cited today. That answer list is your competitive map: it tells you which pages are winning your questions and what they do differently. The fastest way to get brand into ChatGPT answers is to start with the questions where you are already close.
This is slow by hand, which is why the platform exists. DeepSmith generates a starter prompt set from your product, persona, and buyer-stage context, then tracks per-prompt mention and citation rates with full answer history, so the list keeps working after the first afternoon of enthusiasm.
You know this step is done when every priority question has a named current winner beside it.
Where people go wrong: writing prompts in marketing language. Buyers do not ask about "unified content operations platforms." They ask how to stop spending Sunday night on internal linking.
Step 7: Fix what the base model already believes about you
The training path has a hard edge. A knowledge cutoff means anything recent reaches the model only through live retrieval, and nobody outside OpenAI gets to edit the base model's memory on request.
You still have two real moves.
The first is to correct the sources now, so the next training run ingests the right version of you. Update your review profiles, your category listings, and any press coverage carrying stale product details, old pricing, or a positioning you abandoned two years ago.
The second is to let live retrieval carry the correction today. Publish a current, well-structured page that answers the question the outdated source is answering badly, and make sure it is retrievable per steps 2 and 3. When ChatGPT searches, that page is what it finds.
You know this step is done when the top third-party pages describing your product match what your product actually does.
Where people go wrong: updating the homepage and expecting the model to notice. It does not work that way, and the delay is not a sign you did something wrong.
Step 8: Put the work on a cadence and let the data pick your next page
One audit tells you where you stand. A cadence tells you whether anything you did worked.
Run your prompt set weekly with search enabled, in a clean session so personalization does not color the result. For each prompt, record whether your brand is named, whether your domain is linked as a source, which competitors appear beside you, and how the answer shifted since last week.
Roll those observations into four numbers you can actually steer by:
- Mention rate: how often ChatGPT names your brand.
- Citation rate: how often it links your pages as a source.
- Share of voice: your visibility next to competitors.
- Visibility trend: the movement period over period.
Add a per-prompt breakdown so you can see which questions you win, and a page-level view so you know which of your URLs is doing the work. DeepSmith reports all of this in one place, including a competitor leaderboard and the exact competitor pages winning your prompts, then feeds those gaps straight into the content queue. Pro starts at $99 a month and covers ChatGPT.
Then close the loop. Refresh the pages that slipped, rewrite the ones that rank but never get quoted, and widen the prompt set as buyers start asking new questions. That loop is the real work of ChatGPT AEO, and it is the part that separates a one-time audit from a program.
Where people go wrong: reacting to one bad week. Source selection is proprietary and it shifts. Watch the trend, not a single run.
What to do next
You do not need all eight steps this month.
Start with step 2, because it takes an hour and it is the only step that can silently cancel every other one. Then pick your five highest-intent questions and rewrite those pages with an answer block up top. That is a realistic two-week plan, and it is enough to see movement.
The rest, the Wikipedia references, the Reddit presence, the PR, compounds over quarters. Start it now, judge it later.
Keep the whole thing honest with yourself, too. Nobody controls what ChatGPT cites, and any guide on how to get cited in ChatGPT that promises otherwise is selling something. What you control is being fetchable, being findable on Bing, and being the clearest answer on the page. Do those three and you are in the running.
If you would rather not run the tracking spreadsheet and the rewrite queue by hand, start a free DeepSmith trial and see your real prompts, your real gaps, and your first drafts before you pay for anything.
