Pillar guide · LLM SEO

LLM SEO: getting recommended by large language models

By the subding team · Last updated 21 July 2026 · 9 min read

LLM SEO is optimising so large language models like ChatGPT, Claude, Perplexity and Google’s AI recommend you when someone asks them for the best tool in your category. You can’t submit or buy your way in, which makes Reddit GEO one of the few levers you can actually pull. The models learn from and retrieve the open web, and Reddit is one of the sources they lean on most, so a lot of LLM SEO is being genuinely helpful in the right Reddit threads, early.

The short version

LLM SEO is earning a mention inside the answers assistants like ChatGPT, Claude, Perplexity and Google’s AI write, not a ranked page in the blue links.
There’s no form to submit and no slot to buy. The models read and retrieve the open web, so you earn it or you don’t appear.
Reddit carries a lot of the weight, because it’s one of the most-cited sources these models read for recommendation questions.
The durable route is being genuinely helpful, under your own name, in the right threads, and catching them while they’re still live.
subding sits at the noticing end: it flags a relevant thread as it appears so someone on your team can step in. It never posts for you, and no tool can guarantee a mention.

What LLM SEO actually means

When people ask an assistant for the best option in a category, they get a short, opinionated answer with a few names in it. LLM SEO, sometimes written large-language-model SEO, is the work of becoming one of those names. It spans every assistant people actually ask: ChatGPT, Claude, Perplexity and Google’s AI all draw on the same open web, so the job is broadly the same across them.

The mental shift is the important bit. You’re no longer only optimising a page you own; you’re trying to earn a recommendation the model can find and repeat. That recommendation lives out in public, mostly in places where real people compare tools and say what they use.

For the broader picture of how these answers get built, and where Google’s AI overviews sit, read our companion piece on AI visibility. This page stays on the practical question: how do you actually become a name a model repeats.

How LLM SEO differs from classic SEO

Classic SEO gives you levers you can pull directly. You publish pages, tidy up your tags, earn links, and watch a ranking respond over weeks. LLM SEO takes most of those direct levers away and hands you a slower, more indirect one instead.

You can’t submit your way into a model’s answer. You earn your way into it, the same way a friend earns a recommendation.

Here’s the split, laid out plainly so you can see where the effort actually goes.

Classic SEO compared with LLM SEO
Aspect	Classic SEO	LLM SEO
The goal	Rank a page of yours in the blue links.	Get your name into the answer the model writes.
How you get in	Submit sitemaps, earn backlinks, tune on-page tags.	No form and no submission. Models read the open web and retrieve from it live.
What you optimise	Your own pages and their structure.	Where you get talked about, and Reddit threads carry a lot of that weight.
The currency	Keywords, links and crawlable content.	Genuine recommendations from real people who have used you.
How fast it moves	Weeks to a few months once pages are indexed.	Slow. Presence compounds across many threads over time.
How much control you have	A fair amount, over your own site.	Very little. You influence the consensus, you don’t set it.

The two disciplines overlap, and good pages still help. But the row that decides an LLM mention is who’s recommending you in public.

Why Reddit does the heavy lifting

Of all the places the open web keeps opinions, Reddit is unusually valuable to a model. It’s candid, it’s organised by topic, and it’s full of people asking each other what to actually buy or use. That combination is exactly what a model wants when it fields a recommendation question.

This isn’t guesswork. Reddit is one of the most-cited domains in AI answers, and its 2024 content-licensing deals with Google and OpenAI put its threads straight into the pipeline that shapes what assistants suggest. Win a genuine mention in a live thread and you tend to influence several assistants at once.

The threads that matter are the recommendation ones. Somebody weighing options asks the room what people rate, and the replies become the raw material a model can later read back.

r/marketing post · just now

"sorting out our stack this month and would love a steer, what’s the best analytics tool for a small team? happy to hear what people genuinely use day to day."

→ A human joins it while it’s live

Timing is the quiet catch. Those replies land in the first day or so, then the thread settles and gets indexed with its recommendations fixed. Find it late and the advice is already written, and your name isn’t in it. So the unglamorous half of LLM SEO is simply hearing about the right thread early, which is the one part a Reddit monitoring tool can genuinely handle for you.

How to do LLM SEO, step by step

Stripped of the jargon, LLM SEO is being genuinely useful in the right Reddit threads, early and often, under your own name. Here’s the order we’d run it in.

1

Map the questions people ask about your category

Write down the ‘best X for Y’, ‘anyone recommend’ and ‘alternative to’ questions that get asked about a tool like yours. Those questions are where a recommendation can happen at all, and where an LLM later goes looking for one.
2

Find the Reddit threads where those answers get written

Track down the subreddits and recurring threads where people compare options and name what they use. That short list of communities is the ground LLM SEO is actually fought on.
3

Catch new threads while they are still live

A recommendation thread collects its answers in the first day, then settles and gets indexed. Watch your category, your brand and your rivals so you hear about a fresh thread quickly, not after the advice is already written.
4

Reply as a human, genuinely and openly

Answer the real question and mention your product only where it honestly fits. Say you work on it. A useful, disclosed comment earns its place; a disguised advert gets removed and remembered.
5

Keep showing up over months

One good comment won’t change what an assistant says. A steady, honest presence across the threads that matter is what slowly builds your name into the consensus the models read back to people.

Notice that only step three is a job for software. The rest is human work: knowing your category, writing an honest reply, and being patient. subding handles the catching; the genuine, disclosed comment is always yours to write. If your first question is about ChatGPT specifically, our guide on getting mentioned in ChatGPT walks the same ground in more detail.

Engine by engine

The steps above hold everywhere, but each assistant finds its sources differently, and that changes where your effort pays. Jump to the one your buyers use most:

ChatGPT
Perplexity
Google AI Overviews
Google AI Mode
Gemini
Microsoft Copilot
Claude
Grok

ChatGPT OpenAI

Finds sources through: Live web search plus training memory
Reads the live web: Yes, search is built in
Shows citations: Source links shown when it searches
Leans on Reddit: High

When someone asks ChatGPT for a recommendation, it repeats what it has read about the options rather than ranking a page. Getting named comes down to being well-regarded in the sources it trusts, and for buying questions a lot of those are Reddit threads it finds through live search.

Write the plain page that states what you do, who it suits and what it costs, so a search-led answer has something clean to quote.
Show up, disclosed, in the “best tool for” threads ChatGPT tends to search and cite.
Check what it says about your category every few weeks, since live search means the answer drifts.

Does ChatGPT cite Reddit? See its entry in which AI engines cite Reddit.

Perplexity Perplexity

Finds sources through: Its own live search index
Reads the live web: Yes, it retrieves on every answer
Shows citations: Numbered sources on every reply
Leans on Reddit: Very high

Perplexity SEO is unusually literal. Perplexity retrieves live on every question and prints its sources, so the way to be named is simple to state and hard to fake: be in the pages and discussions it pulls for your topic. It is the most source-transparent assistant, which makes it the best place to learn what earns a citation.

Perplexity picks its sources live and shows its working. On every answer it retrieves from its own index, ranks what it finds, and prints numbered citations. That makes it the most transparent assistant about sourcing, and it leans on candid discussion, Reddit included, for opinion-led questions.

Ask Perplexity your own category question and note the numbered sources it chose.
Be present and credible in those exact sources rather than chasing generic authority.
Keep a clear, factual page it can retrieve and quote cleanly under pressure.
Run your own category question and save the numbered sources it cites.
Become genuinely present in those exact sources, not just adjacent ones.

Does Perplexity cite Reddit? See its entry in which AI engines cite Reddit.

Google AI Overviews Google

Finds sources through: The live Google Search index
Reads the live web: Yes, straight from Search
Shows citations: Source links inside the overview
Leans on Reddit: High

Google’s AI Overviews quote the pages and threads that rank for a query, so getting in means being the clearest answer to the exact question and having credible agreement around it, Reddit discussion included. There is no submit button; the overview picks from what Search already surfaces.

Google AI Overviews pick their sources from the live Search index and cite what they summarise. They surface a handful of pages that rank for the query, condense them, and link them, and Reddit is one of the domains they draw on most, which is why forum discussion matters so much here.

Answer the query in the first two sentences of the page, in words the overview can lift whole.
Rank for the long, specific version of the question, which is where overviews appear most.
Be part of the Reddit threads Google ranks for your topic, since overviews link to them often.

Does Google AI Overviews cite Reddit? See its entry in which AI engines cite Reddit.

Google AI Mode Google

Finds sources through: Google Search with query fan-out
Reads the live web: Yes, several live searches per question
Shows citations: Links listed in the side panel
Leans on Reddit: High

Google AI Mode SEO is about breadth. AI Mode takes one question and fans it out into several linked searches, then stitches the results together. So being named is less about winning a single query and more about being present across the many sub-questions it runs, including the forum threads Google now ranks.

Google AI Mode picks its sources by fanning out. It splits your question into several linked searches over Google’s index, gathers sources from each, and assembles one answer. So its sources are broader than a single results page, and the forum threads Google ranks turn up across the sub-queries it runs.

Cover the cluster of sub-questions around your topic, not just the main keyword.
Make each page answer one specific query cleanly, so a fan-out search can land on it.
Be well-regarded in the threads Google ranks for those long-tail questions.
Map the sub-questions around your topic and cover each one clearly.
Give every page one specific query to answer, so a fan-out search can land.

Does Google AI Mode cite Reddit? See its entry in which AI engines cite Reddit.

Gemini Google

Finds sources through: Grounded in Google Search
Reads the live web: Yes, through Google grounding
Shows citations: Grounding links when grounding is on
Leans on Reddit: High

Gemini SEO sits closest to old-school search. Gemini grounds many of its answers in Google’s index, so the pages that rank and the discussions Google now surfaces both shape what it says. If your Google visibility is healthy and people speak well of you in the threads Google favours, Gemini tends to follow.

Gemini picks its sources through Google. When it grounds an answer, it pulls from the same index that powers Google Search, so the pages that rank and the discussions Google surfaces both shape what it says. Since 2024 that increasingly includes Reddit, which Google now ranks far more prominently.

Keep your core pages ranking in Google, since grounding leans on that index.
Structure content so a model can lift a clean, correct sentence from it.
Be genuinely recommended in the forum threads Google now surfaces widely.
Keep your key pages ranking in Google, since grounding leans on that index.
Answer the specific question, not only the head term, so grounding finds you.

Does Gemini cite Reddit? See its entry in which AI engines cite Reddit.

Microsoft Copilot Microsoft

Finds sources through: The Bing search index
Reads the live web: Yes, by way of Bing
Shows citations: Footnote links drawn from Bing
Leans on Reddit: Moderate to high

Microsoft Copilot SEO runs through Bing. Copilot answers from Bing’s index and footnotes the results it used, so the route to being named is earning Bing visibility and being well-regarded in the discussions Bing surfaces. If you have quietly ignored Bing, Copilot is the reason to stop.

Microsoft Copilot picks its sources from Bing. It retrieves from Bing’s index, uses the results to ground its reply, and footnotes them. So Copilot’s sources are Bing’s sources: whatever ranks in Bing and whatever Bing surfaces for the query, candid discussion included.

Verify your site in Bing’s webmaster tools and check how you actually rank there.
Treat Bing visibility as its own task, not a byproduct of your Google work.
Show up in the candid Bing-surfaced threads for your niche.
Verify your site in Bing’s tools and read how you rank there.
Treat Bing as its own channel rather than a spillover from Google.

Does Microsoft Copilot cite Reddit? See its entry in which AI engines cite Reddit.

Claude Anthropic

Finds sources through: Training knowledge, optional web search
Reads the live web: Web search available, not always on
Shows citations: Sources given when web search runs
Leans on Reddit: Low

Claude SEO is the work of becoming the answer Anthropic’s model already trusts. Claude leans on what it learned in training and only sometimes on live web search, so a single hot thread rarely moves it. What moves it is being widely and consistently described as the sensible choice, in clear, well-sourced writing it has read.

Claude picks its sources mostly from training, not a live crawl. It answers from what it learned up to its cut-off, reaching for web search only when a question needs current information. That tilts it toward settled, well-supported material and away from whatever happens to be trending this week.

Publish the plain, structured reference page Claude can lift a sentence from without guessing.
Earn genuine, repeated recommendations rather than one loud mention that fades.
Keep your claims specific and checkable, since vague marketing copy gives a model nothing to stand on.
Build a long, honest trail of recommendation rather than one loud moment.
Publish a clear reference page Claude can quote without hedging.

Does Claude cite Reddit? See its entry in which AI engines cite Reddit.

Grok xAI

Finds sources through: Live web plus posts on X
Reads the live web: Yes, in real time
Shows citations: Web sources and X posts cited
Leans on Reddit: Moderate

Grok SEO is about being current. xAI’s Grok leans hardest on live posts from X for real-time signal, and reaches for open-web sources including Reddit when it needs depth. So being named favours brands people are actively talking about now, rather than those relying on an old reputation.

Grok picks its sources with a bias toward now. xAI’s Grok leans hardest on real-time posts from X, then reaches for live open-web results when a question needs more depth. Reddit is a natural stop for that depth, so considered "is this any good" questions can pull candid threads in.

Be genuinely active where the real-time conversation happens, not just on your own site.
Keep a current, factual reference the open web can point back to.
Be present in the candid threads Grok reaches for when it wants depth.
Be genuinely active where the real-time conversation happens.
Keep a current, factual reference the open web can point to.

Does Grok cite Reddit? See its entry in which AI engines cite Reddit.

What not to do

Every shortcut in LLM SEO works against you, because the source you’re trying to influence is a community that’s very good at spotting manufactured praise. Getting caught doesn’t just waste the effort; it burns the reputation you were building, and it breaks Reddit’s rules.

Don’t, seriously

No spam. Pasting the same pitch across threads reads as spam because it is, and moderators remove it.
No astroturfing. Faking grassroots enthusiasm for your own product is deceptive and rarely survives scrutiny.
No fake accounts or sockpuppets. Running several accounts to praise yourself is bannable, and communities actively look for it.
No bots or auto-replies. Automating comments into threads is precisely the behaviour that got reply-automation tools hunted off Reddit.
No undisclosed promotion. If you’re recommending your own product, say who you are, every time.
No bought upvotes or reviews. A purchased consensus is hollow, and it collapses in public the moment someone notices.
No promises of a guaranteed ranking. Nobody can guarantee a spot in a model’s answer, so don’t buy the claim or make it.

To be plain, subding won’t do any of the above for you, because it can’t and it shouldn’t. It spots the relevant thread and lets you know it exists, nothing more. There is no posting, replying, upvoting or agent acting in your name, and we will never dress up a mention as something we can promise. The noticing is our half; the honest, human reply is yours.

The honest limits

LLM SEO is worth doing, but it’s worth doing with clear eyes. A few things are simply true, and pretending otherwise leads to wasted budget and bad decisions.

No one controls what a model says. You can shift the odds; you cannot set the outcome.
It’s slow by nature. Presence compounds across many threads over months, not in a single afternoon.
A good product still does most of the work. If people don’t genuinely rate you, no amount of thread-watching invents a recommendation.
Any tool, subding included, can only surface the moment. A person still has to show up and say something worth quoting.
Guaranteed placements aren’t real. The honest promise is better odds through early, genuine presence.

Set against those limits, the upside is still large, and most of your competitors won’t do the patient version of this at all. Being genuinely present and early, again and again, is a modest edge that quietly compounds.

Questions, answered straight

What is LLM SEO?

LLM SEO is optimising so large language models like ChatGPT, Claude, Perplexity and Google’s AI recommend you when someone asks them for the best tool in your category. You can’t submit or buy your way in. The models learn from and retrieve the open web, and Reddit is one of the sources they lean on most, so a lot of LLM SEO is being genuinely helpful in the right Reddit threads, early.

How is LLM SEO different from classic SEO, AEO and GEO?

Classic SEO wins a ranked page in the search results. LLM SEO earns a mention inside the answer a model writes, and there’s no page to submit and no position to buy. AEO and GEO are close cousins that overlap heavily with LLM SEO. We untangle the terms in our guides to what AEO is, what GEO is, and how GEO, SEO and AEO relate.

Can I pay to appear in ChatGPT or Claude?

No, and treat anyone who claims otherwise with suspicion. There is no advertising slot, no submission form and no dashboard where you buy a spot in an assistant’s answer. What you can do is become the kind of genuinely recommended option that shows up when the model reads the open web.

Why does Reddit matter so much for LLM SEO?

Reddit is one of the most-cited sources in AI answers, and its 2024 content-licensing deals with Google and OpenAI wired real threads into the systems that shape recommendations. For ‘best tool for X’ questions especially, a lot of the signal traces back to what actual people said on Reddit.

Can subding do LLM SEO for me or get me mentioned?

No. subding is read-and-notify. The moment a relevant Reddit thread appears we ping you in Slack, Discord, email or a webhook, and a person on your team decides whether and how to reply. We never post, comment, upvote or message on your behalf, and we would never claim to guarantee a mention in any model’s answer.

Can any tool guarantee an LLM ranking?

No. Model answers shift constantly and nobody controls them, so a guaranteed placement in ChatGPT, Claude or Perplexity isn’t a thing anyone can honestly sell. A good tool improves your odds by helping you show up early and consistently, which is a very different promise.

How long does LLM SEO take?

Think in months rather than days. A single Reddit comment rarely moves what an assistant says. Consistent, genuinely useful participation across the handful of threads that matter is what gradually builds your brand into the consensus these models read.

Do keywords still matter for ChatGPT SEO?

They help, but they are no longer the main lever. Clear, well-structured pages make it easier for a model to understand and quote you. What tips a recommendation, though, is what other people say about you in places the model reads. You cannot keyword your way into being liked.

Does ranking in normal search help with AI Overviews?

It helps but does not guarantee inclusion. Overviews often draw on well-ranked, clearly written pages, yet they also pull from community sources a ranked page cannot replace. Strong SEO is a foundation, not the whole job.

Can I track AI Overviews without a paid tool?

Partly. You can search your key terms yourself and note when an Overview appears and who it cites, and Search Console hints at shifts in clicks. Dedicated rank tools now flag Overview presence at more scale than manual checks allow.

Be in the thread before the model reads it

You can’t instruct an assistant to recommend you. What you can do is turn up honestly in the Reddit discussions it learns from, and reach them before they settle. Spotting those discussions early is the job subding does.

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