There\u2019s no shortage of advice about how to show up in ChatGPT, Perplexity, and Google AI Overviews. But most of it is organized around the same goals: getting cited and making your content more accessible to LLMs. While these aren\u2019t necessarily bad goals, they miss the point.<\/p>\n
The question worth asking instead is: How do I get LLMs to recommend my brand to someone who\u2019s ready to buy?<\/strong><\/p>\n That might seem like a subtle distinction. You might even be thinking that\u2019s exactly what those other goals are already optimizing for. In our experience, reframing the question this way gives you a very different read on most of the tactics that get passed around as \u201cLLM SEO.\u201d<\/p>\n For example, just because an LLM cites you as a source doesn\u2019t mean your brand is going to show up in its list of recommendations.<\/p>\n In the screenshot above, Melp App (a Microsoft Teams alternative) was cited twice. <\/em>Being cited once<\/em> for a prompt this core to their business \u2014 good team chat software \u2014 would be celebrated as a win by their agency or marketing team. Cited twice? People might pop champagne. But they didn\u2019t show up in the final list of recommended products in ChatGPT\u2019s text output. This happens all the time. We didn\u2019t have to run a bunch of different prompts to find this example; we found it on the first try.<\/p>\n The user experience reality that people aren\u2019t emphasizing enough is that most users aren\u2019t going to dig into the citations. They\u2019re just going to act on whatever\u2019s recommended in the AI response. <\/p>\n Think about your own behavior using LLMs: <\/strong>How often are you scrolling through citations and clicking in to read those articles versus just asking ChatGPT or Claude a follow-up question if you have one?<\/p>\n We\u2019ve been running Grow and Convert since 2017, working with dozens of SaaS and B2B clients. Our focus has always been on bottom-of-funnel content that drives leads and conversions, not traffic. Over the last few years, we\u2019ve found the same principle applies in AI search.<\/p>\n In this article, we\u2019ll share our thoughts on the most common tactics for showing up in LLM chats through the lens of getting more leads. We\u2019ll also explain the framework we use with clients to build AI visibility that actually produces results, and finally, we\u2019ll cover how to measure results.<\/p>\n Below, we walk through the most widely circulated LLM SEO tactics. For each one, we cover what\u2019s being recommended and what the evidence and our experience actually show. Then, we\u2019ll show you where we\u2019d prioritize your time instead.<\/p>\n Note: You\u2019ll see this work labeled a dozen different ways, including GEO (generative engine optimization), AEO (answer engine optimization), AIO, LLMO, or just \u201cAI SEO\u201d. We use GEO, but the label matters less than the approach.<\/p>\n When ChatGPT searches the web to generate a response, it doesn\u2019t run a single search. It breaks the user\u2019s prompt into smaller sub-queries, sometimes called fan-out queries, to pull information from multiple sources simultaneously. A number of SEOs have argued that if you can identify these sub-queries and rank for them, you\u2019ll reliably earn LLM citations. The pitch is essentially: treat fan-out queries like target keywords in traditional SEO, and citations will follow.<\/p>\n But there\u2019s a big problem with the fan-out query approach: you can\u2019t reliably identify the true prompts your buyers are entering in the first place or the fan-out queries that ChatGPT is using for those prompts.<\/p>\n Any software claiming to show you the real prompts driving AI traffic is working with incomplete data at best. LLM providers don\u2019t share this information. Some tools use panels of real users to get an idea of the types of prompts people are typing into LLMs, and that does provide some useful data.<\/p>\n But this misses a larger point about prompts and AI-generated answers: even if you guess the exact prompt a real user types into an LLM, their answer and your answer may be completely different because of context and personalization.<\/strong><\/p>\n This idea of \u201cwe\u2019ll show you what prompts users are typing into ChatGPT\u201d is traditional SEO thinking misapplied to AI search. In AI search, first of all, users don\u2019t enter short, predictable, or repeatable prompts like they do with SEO keywords. They have long conversations, many of which are unique to that user. <\/p>\n But beyond that, the reality is it doesn\u2019t matter much whether you know the exact<\/em> prompt a user is entering because their response and the responses recorded by your AI visibility tool won\u2019t be the same.<\/p>\n When ChatGPT generates a response, it factors in everything it knows about that user: their industry, company size, budget, tools they\u2019ve used before, goals they\u2019ve mentioned in past conversations. <\/p>\n We call this the difference between the literal prompt and the effective prompt. The literal prompt is what they typed. The effective prompt is what ChatGPT actually uses to generate the answer, and it can be the equivalent of a thousand-word brief on that person\u2019s specific situation.<\/p>\n We cover this idea in more detail here<\/a>.<\/p>\n This is why the old SEO mental model of \u201cprompts as keywords with search volume\u201d doesn\u2019t translate to AI search. The personalization is deep enough that in a meaningful sense, every user\u2019s effective prompt is unique. Truly optimizing for all of them would mean producing thousands of pieces of content targeting an infinite number of specific scenarios. That\u2019s not a strategy.<\/p>\n Later, we\u2019ll cover what to do instead.<\/p>\n There are variations of this advice, but the general consensus is that if you publish in-depth content that covers a topic thoroughly, fill it with statistics, and format it correctly, you\u2019re giving LLMs both the authority signals and the citable material they need to surface your brand.<\/p>\n We agree that in-depth content is the right call. That\u2019s been our philosophy for years.<\/p>\n But the version of \u201cin-depth content\u201d that gets passed around in GEO advice is typically vague and missing important points because the advice is focused on getting citations, not on getting recommended as a brand.<\/p>\n Most importantly, almost none of it tells you to write about your product, which is the most important part of a good LLM SEO strategy. So<\/a> while in-depth content is good, if it\u2019s on top-of-funnel topics (not related to products), LLMs may cite it. But it won\u2019t help your cause of getting recommended as a product or solution by LLMs.<\/p>\n Now, if you do write about product-related, bottom-of-funnel topics, then yes, writing in detail matters because buyers describe their situations to LLMs in great detail. Someone using Google types \u201cbest project management software.\u201d Someone using ChatGPT might explain that they\u2019re running a 12-person remote engineering team, their current process is a spreadsheet, they need GitHub and Slack integrations, their budget is around $500 a month, and they\u2019ve tried Asana and found it too rigid.<\/p>\n The LLM takes all of that rich context into account and matches it against everything it knows about available solutions. The solutions it recommends will be ones it thinks can help that user\u2019s highly specific pain points. So, you need to provide LLMs with content that helps make the connection between specific use cases, pain points, and your product or service.<\/p>\n One note before moving on. Original data and specific customer outcomes make good content better, and they give LLMs concrete, verifiable material to draw from. But statistics are a layer on top of a good content strategy, not a substitute for one.<\/p>\n Statistics embedded in top-of-funnel explainers don\u2019t produce brand recommendations. Statistics that help an LLM explain why your product is the right fit for a specific buyer, inside detailed BOF content, do.<\/p>\n There\u2019s a widespread concern that writing specifically about your product hurts AI visibility, that LLMs prefer neutral editorial content and deprioritize anything that looks promotional. In our experience, that\u2019s just not the case.<\/p>\n We always put our clients first in listicles and spend way more time going into detail about the product and how it solves specific pain points than any other competitor on the list. Our clients continue to report getting leads from ChatGPT and other LLMs.<\/p>\n You read more about it here:<\/strong> Self-Promotional Listicles Aren\u2019t the Problem. Bad Content Is<\/a><\/p>\n llms.txt is a file proposed in 2024, similar in concept to robots.txt, intended to give AI systems a curated guide to your site\u2019s most important content. A number of SEO tools added support for generating it, and several widely read publications recommended it as a way to improve how LLMs index and cite you.<\/p>\n We, and others, have tested this, and it makes no measurable difference in AI visibility. Plus, no major AI platform has publicly confirmed they use llms.txt in any meaningful way. A study<\/a> analyzing 300,000 domains found no correlation between having an llms.txt file and AI citation frequency. Even Squirrly, an SEO tool that added llms.txt support specifically because users requested it, was transparent about what they found, saying<\/a> there\u2019s \u201ccurrently zero proof that it helps with being promoted by AI search engines.\u201d<\/p>\n This tactic is alluring because it gives you something concrete and quick to do, but there\u2019s little to no evidence that it actually does anything. The real path to LLM visibility is the harder work of building brand authority and writing detailed content about your product.<\/p>\n Schema markup (FAQ schema, HowTo schema, Organization markup) is commonly recommended as a way to help LLMs parse your content structure and signal authority. The argument is that structured data makes your content easier for AI systems to extract and understand, improving your chances of being cited.<\/p>\n As we covered above, this misses the point. Schema may be worth implementing eventually as part of general site hygiene, but as far as we\u2019re aware, it doesn\u2019t move the needle on AI recommendations and shouldn\u2019t be prioritized over product-specific content. Similar to llms.txt and some of the other items on this list, even if it did work, it\u2019s simply a method of helping LLMs better understand the content on your site, not a way to expose your site to LLMs in the first place.<\/p>\n Many guides recommend rewriting H2 headings as questions and adding FAQ sections to articles. The reasoning is that LLMs process conversational, question-formatted content more naturally than keyword-style headings, and that FAQ structures provide clean, extractable answer blocks that are easy to cite.<\/p>\n However, LLMs don\u2019t need easy-to-read content the way a casual human reader might.<\/p>\n They can sift through dense, unformatted information more capably than most people, and our client results back this up. The content we produced for Constitution Lending, for example, is long, dense material because that\u2019s what the topic calls for. But it appears in the top three AI recommendations for over 50 bottom-of-funnel prompts across ChatGPT, Perplexity, and Google AI Overviews, and none of it relies on FAQ sections or question-formatted headings.<\/p>\n Formatting choices might help give LLMs something to cite, but again, conversions are the end goal, not citations. If you have time to invest in improving your content, use it to expand your coverage of product-specific topics rather than reformatting existing headings.<\/p>\n Getting your brand mentioned on community platforms like Reddit and Quora is widely recommended based on the premise that LLMs pull heavily from user-generated content. There\u2019s also a more general assumption that there\u2019s a list of domains LLMs go to for nearly every search.<\/p>\n We ran the numbers on this. In our study<\/a> analyzing over 100 prompts across five industries, we found that 86% of LLM citations came from industry-specific domains (often the vendors\u2019 own blogs), while generic sites like Reddit and Wikipedia were cited only 16% of the time.<\/p>\n The assumption that there\u2019s a universal list of high-value domains that applies to every business just isn\u2019t how it works. When someone asks ChatGPT for the best dispatch software for trucking companies, it\u2019s not pulling from Reddit \u2014 it\u2019s searching for sources that are actually relevant to that query.<\/p>\n The broader version of this advice is a \u201cbe everywhere\u201d digital PR push \u2014 the idea that you should get your brand mentioned across as many channels as possible so LLMs encounter it frequently. That may actually work because the more your brand name is mentioned (particularly in association with your product space or vertical), the higher the chances that LLMs will know your brand and associate it as a credible solution in your space. But a \u201cbe everywhere\u201d strategy is expensive and time-consuming, something most brands can\u2019t afford.<\/p>\n Below, we cover how to get more specific and target the third-party sources that are actually important for your brand.<\/p>\n A widely repeated recommendation is to strengthen E-E-A-T signals (Experience, Expertise, Authoritativeness, and Trustworthiness). In practice, this usually means adding named authors with stated credentials, including expert review notes on content, and producing material that demonstrates firsthand experience rather than generic research.<\/p>\n This is directionally right, but the mechanism is still Google. E-E-A-T signals help you rank in traditional search, and because LLMs find your content through web searches, better Google rankings increase your LLM visibility. LLMs likely aren\u2019t independently evaluating your author bio and deciding to trust your content more because of it. The credibility signal runs through rankings.<\/p>\n One of the main ways we address this in our own client work is by conducting in-depth interviews with product and sales teams before writing anything, extracting the specific features, differentiators, positioning, and customer outcomes that make each product distinct. That product knowledge is what lets LLMs use as content to match brands to buyers. When ChatGPT recommends a client using language that closely mirrors what\u2019s on their site, it\u2019s because the content taught the LLM specifically when and how to make that recommendation.<\/p>\n This one is for ChatGPT specifically, since it\u2019s widely understood that Bing and ChatGPT have a tight relationship. The idea is that since ChatGPT uses Bing for web search, you need to appear on Bing, not just Google.<\/p>\n We looked at this directly in our fan-out query study<\/a>. We analyzed 100 buying-intent prompts and compared ChatGPT\u2019s cited sources against both Google and Bing SERPs. Only 8% of citations appeared in Bing but not Google, meaning if you\u2019re ranking on Google, you\u2019re already capturing almost all of the search-driven ChatGPT visibility that Bing would add. Optimizing specifically for Bing has minimal marginal value.<\/p>\n However, submitting your sitemap to Bing Webmaster Tools isn\u2019t much work. Just don\u2019t expect it to be the quick fix everyone wants.<\/p>\n We\u2019ll briefly cover the strategy we use for clients here, but if you want a more in-depth explanation, read this article<\/a> or reach out to us.<\/p>\n Owned content is the foundation of any AI search strategy, for a few reasons:<\/p>\n It\u2019s tempting to default to a keyword list (the way traditional SEO strategies always have), but that falls short in AI search, where users describe highly specific, context-rich situations no keyword list can fully anticipate.<\/p>\n The way we think about it is a topic map: a coordinated set of product-specific content covering every angle that matters for your business, including your categories, competitors, use cases, target personas, and the specific pain points your product solves for each.<\/p>\n![\"ChatGPT:](\"https:\/\/www.growandconvert.com\/wp-content\/uploads\/2026\/06\/chatgpt-team-chat-software-for-remote-teams-1024x530.png\")
<\/figure>\n<\/div>\nThe common recommendations (and what we actually think)<\/strong><\/h2>\n
1. Target fan-out queries to get cited<\/strong><\/h3>\n
2. Publish comprehensive, quotable content<\/strong><\/h3>\n
Is talking about your product penalized by LLMs?<\/strong><\/h4>\n
3. Add llms.txt to your site<\/strong><\/h3>\n
4. Use schema markup and structured data<\/strong><\/h3>\n
5. Rewrite headings as questions and add FAQ sections<\/strong><\/h3>\n
6. Get mentioned on Reddit and third-party sites<\/strong><\/h3>\n
7. Build E-E-A-T signals and author credentials<\/strong><\/h3>\n
8. Optimize for Bing<\/strong><\/h3>\n
What We Recommend Doing Instead: Prioritized GEO<\/strong><\/h2>\n
Tier 1: Owned BOF content that ranks on Google<\/strong><\/h3>\n
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