We\u2019ve noticed a lot of online discussion around GEO talks about LLMs like they\u2019re all the same.\u00a0<\/p>\n
- \n
- \u201cHere\u2019s how LLMs work\u2026\u201d<\/li>\n
- \u201cTo get mentioned by LLMs\u2026\u201d<\/li>\n
- \u201cWe studied 10,000 prompts across multiple LLMs\u2026\u201d<\/li>\n<\/ul>\n
But the reality is we\u2019re talking about 6+ different systems that answer questions, recommend brands, and cite sources in different ways. So as marketers, we wanted to ask: Are there differences in how easy it is to influence or get mentioned in each AI tool?\u00a0<\/strong><\/p>\n
The answer, as our data below shows, is yes.<\/p>\n
We track the AI visibility of all our clients in Traqer<\/a>, and we\u2019re seeing a clear trend of three tiers of visibility between the LLMs we\u2019re tracking:\u00a0<\/p>\n
- \n
- AIO and AI Mode<\/span> \u2013 Highest visibility<\/li>\n
- Perplexity and Gemini<\/span> \u2013 Medium<\/li>\n
- ChatGPT<\/span> \u2013 Lowest visibility<\/li>\n<\/ol>\n
Here\u2019s a screenshot of how that data looks in our Traqer dashboard:\u00a0<\/p>\n
\n![\"Traqer](\"https:\/\/www.growandconvert.com\/wp-content\/uploads\/2026\/06\/traqer-ai-visibility-chatgpt-perplexity-aio-gemini-1024x1000.png\")
<\/figure>\n<\/div>\nWe plotted this data to see if we could find patterns in certain LLMs consistently showing higher brand visibility than others. Here is the visibility of 19 clients normalized to each client\u2019s highest visibility LLM (which is plotted as 100%):\u00a0<\/p>\n
\n![\"Traqer](\"https:\/\/www.growandconvert.com\/wp-content\/uploads\/2026\/06\/traqer-top-performing-llm-aio-perplexity-gemini-chatgpt-1024x556.png\")
<\/figure>\n<\/div>\nWe clearly see three tiers of visibility.\u00a0<\/p>\n
Tier 1: The Google SERP LLMs (AIO and AI Mode)<\/strong><\/p>\n
These consistently show the highest visibility across our clients. In fact, either AIO or AI Mode is the highest visibility LLM (plotted as 100%) for all but one client \u2014 and that one is a brand-new client that hasn\u2019t done any SEO and therefore has very little SEO presence. <\/p>\n
As we\u2019ll explain below, this is not a coincidence. AI Overview and AI Mode are not typical LLMs like ChatGPT that answer questions based on their training-data-informed understanding of the world. They behave more like summarizers of what\u2019s ranking on Google for that topic. Our clients are doing well in AIO and AI Mode because we rank them for tons of product-focused SEO keywords.<\/p>\n
Tier 2: Perplexity and Gemini<\/strong><\/p>\n
These, on average, show 40\u201350% of the visibility of the highest-performing LLM for each client. As we\u2019ll discuss below, this suggests that Perplexity and Gemini factor in traditional search results (aka SEO) more than ChatGPT. <\/p>\n
For Perplexity, that\u2019s obvious because it too is a search results summarizer. But the Gemini data is interesting. It suggests that Gemini weighs live search results more than ChatGPT when generating responses.<\/p>\n
Tier 3: ChatGPT<\/strong><\/p>\n
This data confirms what we have noticed for months: ChatGPT is the hardest LLM to influence, at least with traditional SEO. It seems to weigh search results less and rely on its training-data-informed model of the world more.\u00a0<\/p>\n
This has implications for marketing teams, both in terms of setting expectations and for guiding GEO strategy. But the biggest takeaway we want to share with the community is to stop thinking about GEO \/ AEO \/ AIO as one thing. GEO is not one battle, it\u2019s actually multiple separate battles you are fighting simultaneously. <\/strong>And that may, at times, mean deploying unique strategies for each.<\/p>\n
Data and Details<\/h2>\n
This section is for those who want to understand the details behind our data. It gets a bit technical. If you want to get to marketing implications, skip it<\/em>.\u00a0<\/p>\n
The Data<\/strong><\/h3>\n
What is normalized data?<\/strong><\/p>\n
In the above graph, we say the data is \u201cnormalized\u201d to the highest-performing LLM. What that means is we divide the visibility percentages of each LLM by the highest visibility percentage.\u00a0<\/p>\n
For example, take our own data as of this writing:\u00a0<\/p>\n
\n![\"Grow](\"https:\/\/www.growandconvert.com\/wp-content\/uploads\/2026\/06\/grow-and-convert-llm-visibility-2026-1024x106.png\")
<\/figure>\n<\/div>\nThe highest visibility LLM for us right now is AIO at 67%. In the above graph, we normalized these numbers by dividing each LLM\u2019s visibility percentage by 67%. So AIO sits at 100%, AI Mode at 97% (0.65\/0.67), and so on.\u00a0<\/p>\n
Why do we normalize?\u00a0<\/strong><\/p>\n
Normalizing is done whenever you want to see the relative<\/em> difference between data points, which is what we\u2019re trying to do here. We\u2019re trying to learn, across our clients, if some LLMs are easier to get brand visibility in than others. Is it easier to be mentioned or cited in an AIO than ChatGPT, for example? <\/p>\n
If you don\u2019t normalize, you\u2019ll have a brand with 80% visibility across the board and another with 8%. That doesn\u2019t tell you much. So for each brand, we divide each LLM\u2019s visibility number by the highest one to see, relatively speaking,<\/em> which LLMs have higher visibility than the others.\u00a0<\/p>\n
Normalizing in different ways. Same result.\u00a0<\/strong><\/p>\n
Note that we tested normalizing the data in different ways and we still saw the three tiers of visibility.\u00a0<\/p>\n
Above, we showed visibility normalized to each client\u2019s highest visibility percentage. Here is the same data, normalized to each client\u2019s AIO<\/em> visibility percentage. So AIO is 100% for each client.\u00a0<\/p>\n
\n![\"Traqer](\"https:\/\/www.growandconvert.com\/wp-content\/uploads\/2026\/06\/traqer-aio-visibility-perplexity-gemini-chatgpt-1024x522.png\")
<\/figure>\n<\/div>\nThe percentages are different, but the takeaways are the same:\u00a0<\/p>\n
- \n
- AIO and AI Mode are in a tier of their own<\/li>\n
- AI Mode has slightly higher visibility than AIO\u00a0<\/li>\n
- Perplexity and Gemini show about half the visibility of AIO and AI Mode<\/li>\n
- ChatGPT is the toughest, at about a third of the visibility of AIO and AI Mode<\/li>\n<\/ul>\n
Finally, here\u2019s the same data normalized to each client\u2019s average<\/em> visibility percentage across the 5 LLMs we\u2019re tracking:<\/p>\n
\n![\"Traqer](\"https:\/\/www.growandconvert.com\/wp-content\/uploads\/2026\/06\/traqer-average-visibility-aio-perplexity-gemini-chatgpt-1024x526.png\")
<\/figure>\n<\/div>\nAgain, different absolute values, same takeaway.\u00a0<\/p>\n
The Visibility Metric<\/strong><\/h3>\n
The visibility metric we are using is the percent of tracked prompts where the brand is either mentioned by name in the output text or their website is cited as a source.\u00a0<\/p>\n
Separating Brand Mentions vs. Citations<\/strong><\/h3>\n
An obvious follow-up question after seeing this data is: Does it change if we look only at<\/em> mentions of the brand name in the LLM output, not<\/em> citations? Reason being, having your brand mentioned by an LLM in its answer (which almost always means it\u2019s positively recommending your brand) is far more valuable than just being one of the sources.\u00a0<\/p>\n
So we ran this same data analysis using only brand mentions and the results are almost the same, with the key difference being: Perplexity has much lower brand mentions<\/strong> \u2014 it\u2019s in Tier 3 with ChatGPT from a brand mention perspective.\u00a0<\/p>\n
\n![\"Traqer](\"https:\/\/www.growandconvert.com\/wp-content\/uploads\/2026\/06\/traqer-best-performing-llm-aio-perplexity-gemini-chatgpt-1024x724.png\")
<\/figure>\n<\/div>\n(I\u2019m keeping the same color coding as the other graphs for visual clarity).\u00a0<\/p>\n
Another takeaway is that the absolute numbers are lower for ChatGPT visibility when you look at brand mentions only. Not only is ChatGPT harder to get cited relative to Google AI products, but it also looks like getting it to mention your brand in its answer seems to be even harder.\u00a0<\/p>\n
The Prompts<\/strong><\/h3>\n
The prompts, themselves, are important to this discussion. As we\u2019ve explained before<\/a>, in GEO, unlike in SEO, LLMs won\u2019t mention brands when users ask general top-of-funnel questions.\u00a0<\/p>\n
For example, if you ask ChatGPT, \u201cHelp me build a content strategy for AI search\u201d, it\u2019ll answer your question but won\u2019t say something like \u201cGrow & Convert is an agency that can help you\u201d (unless the follow-up conversation naturally moves in that direction). Those top-of-funnel informational prompts shouldn\u2019t be a priority in your GEO strategy.\u00a0<\/p>\n
Instead, we recommend marketing teams focus on \u201cbottom of the funnel\u201d or product-centric topics for GEO because that\u2019s where you actually have a shot at being recommended or mentioned.<\/p>\n
As a result, the prompts we\u2019re tracking for clients, and therefore are part of this study, are bottom-of-funnel and product-centric.\u00a0<\/p>\n
Here are examples:\u00a0<\/p>\n
\n![\"Best](\"https:\/\/www.growandconvert.com\/wp-content\/uploads\/2026\/06\/best-conversational-ai-ticket-resolution.png\")
<\/figure>\n<\/div>\n\n![\"Same](\"https:\/\/www.growandconvert.com\/wp-content\/uploads\/2026\/06\/same-day-business-loans.png\")
<\/figure>\n<\/div>\nNote that our tool Traqer guides you towards these bottom-of-funnel prompts and topics naturally. You can try it and get your own visibility numbers for free at Traqer.ai<\/a>.<\/p>\n
Explaining the Differences: Search-Based vs. Training-Data-Based LLMs<\/h2>\n
The core reason visibility differs in this way is how the LLMs work. Namely, I want to introduce a concept that will help us understand these:\u00a0<\/p>\n
- \n
- Search-based LLMs<\/li>\n
- Training-data-based LLMs.\u00a0<\/li>\n<\/ul>\n
AIO and AI Mode are \u201csearch-based LLMs\u201d because they literally are built to summarize search results. Perplexity actually is, too; it just uses a different search index, as we\u2019ll explain below.\u00a0<\/p>\n
In contrast, ChatGPT, Gemini, and others, while they can and do search the web, are general-purpose LLMs that don\u2019t necessarily need<\/em> to search the web to answer users\u2019 questions. They have an internal model of how the world works that they can use to answer questions.\u00a0<\/p>\n
I find it convenient to think of it visually in this way:<\/p>\n
\n![\"Illustration](\"https:\/\/www.growandconvert.com\/wp-content\/uploads\/2026\/06\/illustration-perplexity-vs-google-vs-bing-search-ai-1024x876.png\")
<\/figure>\n<\/div>\nCaveat: This is intentionally oversimplified to help people quickly grasp the basics. I\u2019m sure there are details and data sources not shown above. For example, Perplexity says they now use their own index, but in reality they may still use Google and Bing to supplement their index (they list both as \u201csubprocessors\u201d <\/em>on their site<\/em><\/a>); people debate how much ChatGPT uses Google versus Bing; and so on. So don\u2019t take this diagram as the ultimate truth, but rather a convenient and quick mental model.\u00a0<\/em><\/p>\n
So the fundamental reason for the differences in visibility is that we rank our clients for relevant SEO terms on Google<\/em>. <\/strong><\/p>\n
LLMs that rely more heavily on Google search results (e.g., AIO and AI Mode) mention or cite our clients more often.<\/strong><\/p>\n
LLMs that rely less on Google search (e.g., ChatGPT) are harder to influence through SEO rankings.\u00a0<\/strong><\/p>\n
AI Overview and AI Mode are \u201cRAG\u201ds Built to Summarize Search Results\u00a0<\/strong><\/h3>\n
On this page<\/a> of Google\u2019s own documentation, they explain how AI Overview and AI Mode are based on search results. Here\u2019s a critical line from that page saying they use AI to highlight content from their search index:<\/p>\n
\u201cThe best practices for SEO continue to be relevant because our generative AI features on Google Search are rooted in our core Search ranking and quality systems. These features rely on AI techniques <\/em>to highlight content from our Search index.<\/em><\/strong>\u201d<\/em> (emphasis mine)<\/p>\n
That page goes on to say these AI features on the SERP work as a retrieval-augmented generation system (RAG), meaning they are \u201crelying on our core Search ranking systems to retrieve relevant, up-to-date web pages from our Search index\u201d<\/em>.\u00a0<\/p>\n
\n![\"Google](\"https:\/\/www.growandconvert.com\/wp-content\/uploads\/2026\/06\/google-search-central-is-seo-still-relevant-generative-ai-search-1024x323.png\")
<\/figure>\n<\/div>\nFinally, look at the note on \u201cQuery fan-out\u201d. This helps explain why AI Overview or AI Mode doesn\u2019t exactly<\/em> mimic the organic results below. It\u2019s because when a user enters a search query, Google runs additional related queries and synthesizes all of the results into one answer.\u00a0<\/p>\n
In short, when a user searches in Google, AI Overview and AI Mode read through the traditional organic search results (for your query and related queries) and use them to generate their responses. So it\u2019s no wonder we\u2019re seeing very high AI visibility numbers for our clients, who we\u2019ve already ranked on Google for all kinds of bottom-of-funnel, high-buying-intent SEO keywords.<\/strong><\/p>\n
You can see multiple examples and screenshots of one of our clients being cited and mentioned in AI Overview or AI Mode in this case study<\/a>.\u00a0<\/p>\n
Perplexity Is Also Search-Based, but Its Index Is Different Than Google\u2019s<\/strong><\/h3>\n
Perplexity\u2019s CEO has said the same thing about how it works, and actually gone further to say Perplexity is explicitly built to not say anything that isn\u2019t supported by the search results.\u00a0<\/em><\/p>\n
This is articulated nicely by Ethan Lazuk, who quotes the Perplexity CEO on Lex Fridman\u2019s podcast<\/a>:\u00a0<\/p>\n
\n![\"Perplexity](\"https:\/\/www.growandconvert.com\/wp-content\/uploads\/2026\/06\/perplexity-ceo-aravind-srinivas-how-responses-are-generated-1024x384.png\")
<\/figure>\n<\/div>\nPerplexity is literally using this feature as a differentiator: not only do we rely on search results, we are also preventing our tool from hallucinating by forcing it to not say anything that the search results don\u2019t say<\/em>.\u00a0<\/p>\n
Then why are we seeing lower visibility in Perplexity than in AIO and AI Mode? If it\u2019s also fully reliant on search results, shouldn\u2019t we also see Tier 1-level visibility numbers for Perplexity?<\/p>\n
Frankly, we were surprised by this. While we don\u2019t know for certain, the most reasonable explanation is that Perplexity\u2019s search index is different from Google\u2019s, so its results are not going to be the same.\u00a0<\/strong><\/p>\n
Specifically, Perplexity\u2019s own engineering blog<\/a> confirms they used to<\/em> use 3rd party search APIs (aka Google, Bing, etc.) but that got expensive and didn\u2019t scale, so they built their own crawler and index. They also actively decide which documents to keep \u201chot,\u201d meaning their retrieval works fundamentally differently than Google\u2019s.<\/p>\n
Multiple<\/a> sources<\/a> have also noted that Perplexity\u2019s index is smaller than Google\u2019s.\u00a0\u00a0<\/p>\n
In other words, yes, Perplexity is also a search-based LLM, but it\u2019s not Google, and therefore its results don\u2019t perfectly overlap with Google\u2019s.\u00a0<\/p>\n
The GEO implication, from our data, is that if you rank well on Google (as our clients do), you can expect around half the visibility for bottom-of-funnel prompts in Perplexity as you see in AIO and AI Mode.\u00a0<\/p>\n
Training-Data-Based LLMs: Gemini and ChatGPT<\/h2>\n
In contrast, ChatGPT and Gemini don\u2019t need to rely on search results to answer a user\u2019s question. They already have an existing understanding of the world, including your product space, built into them. AI researchers call this \u201cparametric memory\u201d: the models were given famously large amounts of data when they were first trained, and they converted that data into mathematical weights and patterns about how concepts in the world work. Those weights are called \u201cparameters,\u201d hence the term.<\/p>\n
They Can Recommend Products from Training Data Alone<\/strong><\/h3>\n
This parametric memory includes an understanding of your<\/em> product space. That\u2019s why even if you turn search off or ask ChatGPT to recommend products without searching the web, it can. Here\u2019s an example.<\/p>\n
\n![\"ChatGPT:](\"https:\/\/www.growandconvert.com\/wp-content\/uploads\/2026\/06\/chatgpt-top-crm-b2b-saas-company-without-web-713x1024.png\")
<\/figure>\n<\/div>\nChatGPT doesn\u2019t need to search the web to recommend HubSpot, Salesforce, and Pipedrive. Those are household names in CRMs, so they must have been all over its training data, and it recommends them easily.\u00a0<\/p>\n
This existing understanding of the world is what you are fighting against <\/strong>when you\u2019re trying to do \u201cGEO\u201d on these training-data-based LLMs.<\/p>\n
Search-Based LLMs Can\u2019t Recommend Products Without Searching<\/strong><\/h3>\n
Note that if you ask any of the search-based LLMs to not search the web, they can\u2019t do it. They are built to search the web.<\/strong> That\u2019s how they get their information.\u00a0<\/p>\n
Here\u2019s Google AIO\u2019s response to the same prompt. It searches the web every time, even though I asked it not to. Every bullet and paragraph has a citation:<\/p>\n
\n![\"Google](\"https:\/\/www.growandconvert.com\/wp-content\/uploads\/2026\/06\/google-serps-top-crm-b2b-saas-company-1024x688.png\")
<\/figure>\n<\/div>\nAnd here\u2019s Perplexity\u2019s answer. It literally says \u201cSearching the web\u201d as it thinks\u2026<\/p>\n
\n![\"Perplexity:](\"https:\/\/www.growandconvert.com\/wp-content\/uploads\/2026\/06\/perplexity-searching-the-web-top-crm-b2b-saas-company.png\")
<\/figure>\n<\/div>\nHow Much Does ChatGPT Weigh Its Parametric Memory vs. Search Results?\u00a0<\/strong><\/h3>\n
Of course, no real user will type \u201cWithout searching the web\u201d at the beginning of their prompts. They\u2019ll just ask for product recommendations; for product and brand queries specifically, in our experience, basically every LLM, including ChatGPT, almost always searches the web.\u00a0<\/p>\n
Here\u2019s me asking ChatGPT the same question but removing \u201cWithout searching the web\u201d in my prompt. Note how even though it recommends HubSpot and Salesforce as the top two options again, this time it cites a source for each:\u00a0<\/p>\n
\n![\"ChatGPT:](\"https:\/\/www.growandconvert.com\/wp-content\/uploads\/2026\/06\/chatgpt-top-crm-b2b-saas-company-with-sources.png\")
<\/figure>\n<\/div>\nThis takes us to the central question of how to influence training-data-based LLMs\u2019 responses: How much are they actually factoring in what the search results say?<\/strong><\/p>\n
The short answer is people don\u2019t really know.<\/strong> Above, it\u2019s notable that ChatGPT recommended HubSpot and Salesforce both times I asked: with and without searching the web. So is it using the search results to inform <\/em>its decision or to justify<\/em> the decision that it already made from its parametric model? It\u2019s not clear.<\/p>\n
Query Burst\u2019s article<\/a> on this topic talks about \u201cthe Nike effect\u201d, which is basically exactly what I\u2019m showing above with CRMs: if you asked ChatGPT about running shoes and hypothetically none of the search results it pulled up mentioned Nike, ChatGPT would probably still recommend Nike because Nike is all over its training data.\u00a0<\/p>\n
SparkToro\u2019s study<\/a> on how much variance there is in the brands that LLMs recommend also hints at a similar conclusion. They ran the same prompts asking for products multiple times and saw huge variability in the brands LLMs recommended. But they note that despite this variety, the top 3 most popular products in each category are included in the answer 64\u201373% of the time<\/em>. That very likely means if you are baked into the parametric model as one of the most popular brands in your space, you\u2019re likely to get recommended regardless of what the search results say.<\/p>\n
And, like I said above, the challenge for smaller brands is if you aren\u2019t<\/em> one of the top brands in your space, it\u2019s harder to get mentioned by these training-data-based LLMs. That\u2019s why we see lower visibility numbers for ChatGPT compared to the search-based ones.\u00a0<\/p>\n
Gemini Seems to Weigh Search More Than ChatGPT<\/strong><\/h3>\n
Notably, even though I\u2019m including Gemini in the \u201ctraining-data-based LLMs\u201d category, it also searched the web and cited sources even when I asked it not to.\u00a0<\/p>\n
\n![\"Gemini:](\"https:\/\/www.growandconvert.com\/wp-content\/uploads\/2026\/06\/gemini-top-crm-b2b-saas-company.png\")
<\/figure>\n<\/div>\nAnd that takes us to why our clients, on average, have higher visibility in Gemini versus ChatGPT.\u00a0<\/p>\n
\n![\"Traqer:](\"https:\/\/www.growandconvert.com\/wp-content\/uploads\/2026\/06\/traqer-gemini-vs-chatgpt-visibility.png\")
<\/figure>\n<\/div>\nOn paper, Gemini is a general-purpose LLM like ChatGPT, not<\/em> a search summarizer like AI Overview, AI Mode, or Perplexity. It has a \u201cparametric model\u201d of how the world works. You can ask it to make you an image or do spreadsheet math \u2014 tasks that AIO, AI mode, or Perplexity can\u2019t do. <\/p>\n
But our data points to something else: Gemini seems to rely more on search results to produce its answer than ChatGPT<\/strong> (and perhaps other general-purpose LLMs).\u00a0<\/p>\n
It also seems to search the web way more often, as the example above shows on CRMs. To test this, I also asked it to just explain the basic principles of SEO to me, thinking that this should not require a web search. But it still searched the web and cited sources when ChatGPT didn\u2019t.\u00a0<\/p>\n
\n![\"Gemini](\"https:\/\/www.growandconvert.com\/wp-content\/uploads\/2026\/06\/gemini-vs-chatgpt-citing-sources-1024x559.png\")
<\/figure>\n<\/div>\nI did manage to get Gemini to not<\/em> search the web when I asked it to give me a basic overview of Newtonian physics, so it can<\/em> answer some questions without a search. But what we\u2019re seeing in these anecdotes and our data is that it seems to search the web more than ChatGPT.\u00a0<\/p>\n
Other<\/a> people<\/a> have noted this as a benefit of Gemini whenever you need up-to-date, accurate information. It\u2019s better at searching; it relies on search more, so it hallucinates less.\u00a0<\/p>\n
But our data above pokes at this in a way that\u2019s more interesting for marketers<\/em>: being visible in traditional search (SEO) seems to give you higher visibility in Gemini than in ChatGPT, since Gemini both searches more and seems to integrate those search results into its answers more. <\/strong>You can set expectations with yourself, your leadership, or your clients accordingly.<\/p>\n
Strategies to Get Recommended by Training-Data-Based LLMs<\/h2>\n
1. Branded Content Is Influencing ChatGPT Visibility<\/strong><\/h3>\n
First, despite ChatGPT having lower visibility scores than the other LLMs, we do see growing<\/em> visibility there for our clients, including for brands that are not the well-known incumbents.\u00a0<\/p>\n
Here\u2019s ChatGPT visibility for relevant topics for our real estate lending client, Constitution Lending, who is not an incumbent in their space:<\/p>\n
\n![\"Constitution](\"https:\/\/www.growandconvert.com\/wp-content\/uploads\/2026\/06\/constitution-lending-chatgpt-visibility-1024x309.png\")
<\/figure>\n<\/div>\nOr Toro TMS, also a new-ish player in their space:\u00a0<\/p>\n
\n![\"Toro](\"https:\/\/www.growandconvert.com\/wp-content\/uploads\/2026\/06\/toro-tms-chatgpt-visibility-1024x309.png\")
<\/figure>\n<\/div>\nOr even for us:\u00a0<\/p>\n
\n![\"Grow](\"https:\/\/www.growandconvert.com\/wp-content\/uploads\/2026\/06\/grow-and-convert-chatgpt-visibility-1024x309.png\")
<\/figure>\n<\/div>\nThese aren\u2019t hockey sticks, which, as per everything we stated above, is not going to happen because ChatGPT seems to be harder to influence. But it\u2019s growth nonetheless.\u00a0<\/p>\n
2. Citation Outreach<\/strong><\/h3>\n
As per our prioritized GEO pyramid, owned content isn\u2019t the only lever you can pull to influence LLMs. You also have Tier 2: Off-site Mentions.\u00a0<\/p>\n
\n![\"Prioritized](\"https:\/\/www.growandconvert.com\/wp-content\/uploads\/2025\/12\/image-4-1024x640.png\")
<\/figure>\n<\/div>\nOff-site mentions means reaching out to other sites (besides your own) that are being cited by LLMs in the topics you\u2019re interested in and asking them to include or mention your brand.\u00a0<\/p>\n
How do you figure out what sites those are? You can do it manually (by just asking ChatGPT a prompt that you want to show up for and see who it cites), but it\u2019s more efficient to use AI visibility software that will list all the cited sources for the prompts you are tracking.\u00a0<\/p>\n
For example, here are the citations used for the topic \u201ccontent marketing agency\u201d using our tool, Traqer<\/a>.<\/p>\n
\n![\"Traqer](\"https:\/\/www.growandconvert.com\/wp-content\/uploads\/2026\/06\/traqer-topic-analysis-content-marketing-agency-1024x623.png\")
<\/figure>\n<\/div>\nOne important caveat when doing this: don\u2019t just assume Reddit is the main place where you need to get mentioned; actually look at the sites being cited for your prompts of interest. In the example above, no Reddit threads appear as the top sources.<\/strong><\/p>\n
As we show in detail in this study<\/a>, for most product queries, industry publications are cited way more than general sites like Reddit or Wikipedia.\u00a0<\/p>\n
3. Topic-Based Visibility (Not Single Prompts) Is What Matters In The Real World<\/strong><\/h3>\n
There is an extremely important difference between GEO and SEO that most people overlook: in GEO (AI search), we don\u2019t really know what users are typing into LLMs. And even if we did, we can\u2019t reproduce the output a real user gets because their output will be heavily personalized to their<\/em> conversation history and context (our full explanation of this phenomenon is here: Invisible Prompts<\/a>).\u00a0<\/p>\n
This context and personalization in generating answers mean LLMs recommend products and services to users based on extremely specific <\/em>details about their situation and pain points.\u00a0<\/p>\n
While that makes measuring your true AI visibility hard (your visibility tool won\u2019t be able to see the responses that real users get because that\u2019s personalized to them), it also gives you an avenue to gain visibility: Publish content that shows how your product or service solves very specific problems for very specific users.\u00a0<\/strong><\/p>\n
We discuss this at length in Topic-Based GEO<\/a>.\u00a0<\/p>\n
Final Thoughts<\/h2>\n
- Perplexity and Gemini<\/span> \u2013 Medium<\/li>\n
- AIO and AI Mode<\/span> \u2013 Highest visibility<\/li>\n