If you want your company or brand to be recommended by large language models, you need to be tracking the pages and websites they cite. In other words, you need to be simulating the kinds of prompts that your ideal customer might be typing into these large language models. And then pay attention to what pages online the large language models look at before they recommend you or your competitors.
This is really the entire game right now. If those pages cited by the large language models recommend your brand, the large language model will recommend your brand. We've been tracking this for our clients for about six months now.
We currently simulate about 4,000 prompts every day across chat, G, b, t, Gemini, and perplexity. We use software called P dot AI to do this, which I highly recommend. I was actually just on a call with someone from peak yesterday, and he was telling me that a lot of people are wondering what to do with all this information.
They're tracking these prompts, they're gathering citations, and now they don't know how to actually influence the large language models to recommend their brand. Talk about this stuff a lot on this channel, but I figure I just tell you what I told him. So we just have it all in one video.
So the kind of prompts that we typically track are, I'm this kind of person with this kind of problem looking for this kind of company. There are other types of prompts that you can track and there's good reason to track them, but these are the ones that are most valuable to our clients. The first thing that's important to point out is that the citations from these large language models are going to vary wildly by industry.
So any advice about, hey, ChatGPT really likes this website or hey, you should be on this website cause Google sites it al
to vary wildly by industry. So any advice about, hey, ChatGPT really likes this website or hey, you should be on this website cause Google sites it all the time. None of that advice is generally true across industries.
You really need to be tracking prompts of people looking for the type of service or product that you offer. Let's talk about directories. Typically, there's gonna be between one and 5 directories that large language models often refer to when people are asking about your industry.
Might be other directories that are referenced 1 in 1,000 prompts, 1 in 10,000 prompts, but there's typically going to be a few that are referenced between 1 and 50% of the time someone asks about your industry. You might hear people saying you have to be in all the business directories cause large language models are looking there. No, not really.
Find the 1 to 5 that actually matter and get your business there. Next, let's talk about listicles. Large language models love to cite listicles.
I would say for most industries, it's the most important factor whether or not your company is showing up in the listicles that the large English model Finds. The most common type of listicle, however, is one that's automatically generated by a directory. These are like Yelp's listicles and typically there's gonna be one directory where most listicles are being cited from and it's gonna differ based on the industry.
So figure out what that one directory is for your industry and figure out how to get to the top of those listicles. Typically this is gonna be from getting more reviews or for paying for placement and a lot of times it is worth paying. Right now outside of directories, the other listicles you can be on might be on a blog post, a LinkedIn article, a medium article.
Typically it's
ng. Right now outside of directories, the other listicles you can be on might be on a blog post, a LinkedIn article, a medium article. Typically it's going to be very difficult to get your brand added to these listicles.
A lot of them are going to be created by your competitors. However, it also takes about 30 seconds to send an email and ask what it takes to get on their listicle. So I think it's worth the effort.
In our experience, we have about a 5% success rate. Most of the time, though, they're gonna ask for money or they're gonna ask for a reciprocal link. However, if it's a page that's often cited by large language models, it's probably worth it.
The next one you'll see a lot is discussion threads. Typically this is gonna be Reddit or Facebook groups. The thing to point out here is it's typically the same five to 10 threads that are being cited.
Over and over again. So we might identify 500 threads that have been cited, but 80% of the citations will go to those top 10. And these are typically threads that are at least a year old.
So we simply identify the most cited threads and leave a comment mentioning the brand. Now we're not gonna be overtly promotional just in case a moderator happens to look at it, but we'll mention the brand and the service or product they offer as a way to qualify the response. And of course, in some cases, the large language models are going to be citing your website directly.
In that case, you just want to make sure on those pages it explains exactly what products or services you offer and it recommends you. I know this sounds silly, but a lot of times the content on your website doesn't explicitly recommend you and when the large language model cites you, it's going to retrieve a block of text and if in that text isn't the recommendati
xplicitly recommend you and when the large language model cites you, it's going to retrieve a block of text and if in that text isn't the recommendation for your brand, it actually doesn't matter that it cited your website, it's not gonna recommend you in the response. So those are all the ways that you can influence the content that's on the pages large language models are already citing. However, in our experience, we have a lot more success influencing these responses by creating content ourselves.
I feel like most of the advice online right now centers around influencing the content on the Pages that our senior scholars are already citing and they completely gloss over the fact that you can also replace the pages large language models are citing. And that's typically much easier to do.
Just gather a list of all the articles and pages that are being cited by the large language models, make a list of the titles of all those articles or pages, and then create pieces of content targeting those same keywords on your website, on LinkedIn, on medium, on YouTube, on TikTok, on Instagram. And this is why AI optimization is so much easier than SEO right now. The keywords that these large language models are searching for are way more specific than the terms that humans would search for in Google.
So there's way less competition. All you need is a good system for quickly generating content that you're proud of. I recommend finding a way where each piece of content can be repurposed 10 times on 10 different platforms.
And of course, if you need help, let me know. That's what we do.
