AI SEO: How To Understand AI Mode Rankings via @sejournal, @martinibuster

A simplified explanation of how Google ranks content is that it is based on understanding search queries and web pages, plus a number of external ranking signals. With AI Mode, that’s just the starting point for ranking websites. Even keywords are starting to go away, replaced by increasingly complex queries and even images. How do you optimize for that? The following are steps that can be taken to help answer that question.

Latent Questions Are A Profound Change To SEO

The word “latent” means something that exists but cannot be seen.  When a user issues a complex query the LLM must not only understand the query but also map out follow-up questions that a user might ask as part of an information journey about the topic. Those questions that comprise the follow-up questions are latent questions. Virtually every query contains latent questions.

Google’s Information Gain Patent

The issue of latent queries poses a new problem for SEO: How do you optimize for questions that are unknown? Optimizing for AI search means optimizing for the entire range of questions that are related to the initial or head query.

But even the concept of a head query is going away because users are now asking complex queries which demand complex answers. This is precisely why it may be useful for AI SEO purposes to optimize not just for one query but for the immediate information needs of the user.

How does Google understand the information need that’s hidden within a user’s query? The answer is found in Google’s Information Gain Patent. That patent is about ranking a web page that is relevant for a query then afterward ranking other web pages that have different but related content.

Identify The Latent (Hidden) Questions

One way to look at AI search results is to break them down into the questions that the AI answers are satisfying, to identify the hidden query fan-out questions.

For example, if you ask Google’s AI Mode how to make pizza dough the AI Mode will generate a pizza dough recipe. The answer is synthesized from multiple queries.

There is a way to extract the hidden (latent) questions by using a Reverse Question Answering Prompt, which I’ll show below.

Here is an example of an initial query and the additional query fan-out questions that are involved:

Initial Query: How to make pizza dough?

Query Fan-Out Questions:

• What ingredients are needed to make pizza dough?
• How much pizza dough does this recipe make?
• What indicates that the yeast is active?
• How long should the dough be kneaded by hand?
• How long should the dough be kneaded with a mixer?
• How long should the dough rise during the first proofing?
• How can you develop more complex flavor in the dough?
• What should the oven temperature be for baking the pizza?
• How long should the pizza bake?

Actual AI Mode Question And Answers

Here are some of the hidden query fan-out questions accompanied by the answers that Google’s AI Mode generated.

Question: What ingredients are needed to make pizza dough?
Answer: “1 and 1/3 cups warm water (around 105-110°F or 40-43°C); 1 tablespoon granulated sugar (or honey); 1 packet active dry or instant yeast (2 1/4 teaspoons); 3 1/2 to 4 cups all-purpose or bread flour, plus more for dusting; 2 tablespoons extra-virgin olive oil, plus more for greasing; 1 teaspoon salt.”

Question: How much pizza dough does this recipe make?
Answer: “This recipe makes enough dough for two 10-12 inch pizzas.”

Question: What indicates that the yeast is active?
Answer: “Cover and let it sit for about 5-10 minutes, until the mixture is foamy and bubbly. This indicates the yeast is active.”

Screenshot Of The Actual AI Mode Answer

Reverse Question Answering Prompt

You can use the Reverse Question Answering Prompt to identify the underlying questions in any AI Mode answer. You can even use a similar but more precise prompt to analyze your own content to identify what questions the document answers. It’s a good way to check if your content does or does not answer the questions you want it to answer.

Prompt To Extract Questions From AI Mode

Here is the prompt to use to extract the hidden questions within an AI Mode answer:

Analyze the document and extract a list of questions that are directly and completely answered by full sentences in the text. Only include questions if the document contains a full sentence or sentences that clearly answers it. Do not include any questions that are answered only partially, implicitly, or by inference.

For each question, ensure that it is a clear and concise restatement of the exact information present. This is a reverse question generation task: only use the content already present in the document.

For each question, also include the exact sentences from the document that answer it. Only generate questions that have a complete, direct answer in the form of a full sentence or sentences in the document.

Reverse Question Answering Analysis For Web Content

The previously described prompt can be used to extract the questions that are answered by your own or a competitor’s content. But it will not differentiate between the core search queries the document is relevant for and other questions that are ancillary to the main topic.

To do a Reverse Question Answering analysis with your own content, try this more precise variant of the prompt:

Analyze the document and extract a list of questions that are core to the document’s central topic and are directly and completely answered by full sentences in the text.

Only include questions if the document contains a full sentence or contiguous sentences that clearly answers it. Do not include any questions that are answered only partially, implicitly, or by inference. Crucially, exclude any questions about supporting anecdotes, personal asides, or general background information that is not the main subject of the document.

For each question, ensure that it is a clear and concise restatement of the exact information present. This is a reverse question generation task: only use the content already present in the document.

For each question, also include the exact sentences from the document that answer it. Only generate questions that have a complete, direct answer in the form of a full sentence or sentences in the document.

The above prompt is meant to emulate how an LLM or information retrieval system might extract the core questions that a web document answers, while ignoring the parts of the document that aren’t central to its informational purpose, such as tangential commentary that do not directly contribute to the document’s main topic or purpose.

Cultivate Being Mentioned On Other Sites

Something that is becoming increasingly apparent is that AI search tends to rank companies whose websites are recommended by other sites. Research by Ahrefs found a strong correlation between sites that appear in AI Overviews and branded mentions.

According to Ahrefs:

“So we looked at these factors that correlate with the amount of times a brand appears in AI overviews, tested tons of different things, and by far the strongest correlation, very, very strong correlation, almost 0.67, was branded web mentions.

So if your brand is mentioned in a ton of different places on the web, that correlates very highly with your brand being mentioned in lots of AI conversations as well.”

Read: Data Shows Brand Mentions Boost AI Search Rankings

This finding strongly suggests that visibility in AI search may depend less on backlinks and more on how often a brand is discussed across the web. AI models seem to learn which brands are recommended by how often those sites are mentioned across other sites, including sites like Reddit.

Post-Keyword Ranking Era

We are in a post-keyword ranking era. Google’s organic search was already using AI and a core topicality system to better understand queries and the topic that web pages were about. The big difference now is that Google’s AI Mode has enabled users to search with long and complex conversational queries that aren’t necessarily answered by web pages that are focused on being relevant to keywords instead of to what people are actually looking for.

Write About Topics

Writing about topics seems like a straightforward approach but what it means depends on the context of the topic.

What “topic writing” proposes is that instead of writing about the keyword Blue Widget, the writer must write about the topic of Blue Widget.

The old way of SEO was to think about Blue Widget and all the associated Blue Widget keyword phrases:

Associated keyword phrases

• How to make blue widgets
• Cheap blue widgets
• Best blue widgets

Images And Videos

The up to date way to write is to think in terms of answers and helpfulness. For example, do the images on a travel site communicate what a destination is about? Will a reader linger on the photo? On a product site, do the images communicate useful information that will help a consumer determine if something will fit and what it might look like on them?

Images and videos, if they’re helpful and answer questions, could become increasingly important as users begin to search with images and increasingly expect to see more videos in the search results, both short and longform videos.

Read:

Featured Image by Shutterstock/Nithid

Ahrefs Data Shows Brand Mentions Boost AI Search Rankings via @sejournal, @martinibuster

The latest Ahrefs podcast shares data showing that brand mentions on third-party websites help improve visibility across AI search surfaces. What they found is that brand mentions correlate strongly with ranking better in AI search, indicating that we are firmly in a new era of off-page SEO.

Training Data Gets Cited

Tim Soulo, CMO of Ahrefs, said that off-page activity that increases being mentioned on other sites improves visibility in AI search results, both those based on training data and those drawing from live search results. The benefits of conducting off-page SEO apply to both. The only difference is that training data doesn’t get into LLMs right away.

Tim recommends identifying where your industry gets mentioned:

“You just need to see like where your competitors are mentioned, where you are mentioned, where your industry is mentioned.

And you have to get mentions there because then if the AI chatbot would do a search and find those pages and create their answer based on what they see on those pages, this is one thing.

But if some of the AI providers will decide to retrain their entire model on a more recent snapshot of the web, they will use essentially the same pages.”

Tim cautioned that AI companies don’t ingest new web data for training and that there’s a lag in months between how often large language models receive fresh training data from the web.

Appear On Authoritative Websites

Although Tim did not mention specific tactics for obtaining brand mentions, in my opinion, off-page link-building strategies don’t have to change much to build brand mentions.

Tim underlined the importance of appearing on authoritative websites:

“So yeah, …essentially it’s not that you have to use different tactics for those things. You do the same thing, you appear like on credible websites, but yeah, let’s continue.”

The only thing that I would add is that authoritativeness in this situation is if a site gets mentioned by AI search. But the other thing to think about is if a site is simply the go-to for a particular kind of information, relevance

Topicality Of Brand Mentions

The other thing that was discussed is the topicality of the brand mentions, meaning the context in which the brand is discussed. Ryan Law, Ahrefs’ Director of Content Marketing, said that the context of the brand mention is important, and I agree. You can’t always control the narrative, but that’s where old-fashioned PR outreach comes in, where you can include quotes and so on to build the right context.

Law explained:

“Well, that segues very nicely to what I think is probably the most useful discrete tactic you can do, and that is building off-site mentions.

A big part of how LLMs understand what your brand is about and when it should recommend it and the context it should talk about you is based on where you appear in its training data and where you appear on the web.

• What topics are you commonly mentioned alongside?
• What other brands are you mentioned alongside?

I think Patrick Stox has been referring to this as the era of off-page SEO. In some ways, the content on your own site is not as valuable as the content about you on other pages on the web.”

Law mentioned that these off-page mentions don’t have to be in the form of links in order to be useful for ranking in AI search.

Testing Shows Brand Mentions Are Important

Law went on to say that their data shows that brand mentions are important for ranking. He mentions a correlation coefficient of 0.67, which is a measure of how strongly two variables are related.

Here are the correlation coefficient scales:

• 1.0 = perfect positive correlation (two things are related).
• 0.0 = no correlation.
• –1.0 = perfect negative correlation (for example, for every minute you drive the distance gets smaller, a negative correlation).

So, a correlation coefficient of 0.67 means that there’s a strong relationship in what’s observed.

Law explained:

“And we did indeed test this with a bit of research.

So we looked at these factors that correlate with the amount of times a brand appears in AI overviews, tested tons of different things, and by far the strongest correlation, very, very strong correlation, almost 0.67, was branded web mentions.

So if your brand is mentioned in a ton of different places on the web, that correlates very highly with your brand being mentioned in lots of AI conversations as well.”

He goes on to recommend identifying industry domains that tend to get cited in AI search for your topics and try to get mentioned on those websites.

Law also recommended getting mentions on user-generated content sites like Reddit and Quora. Next he recommended getting mentioned on review sites and on YouTube video in the transcripts because YouTube videos are highly cited by AI search.

Ahrefs Brand Radar Tool

Lastly, they discussed their Ahrefs tool called Brand Radar that’s useful for identifying domains that are frequently mentioned in AI search surfaces.

Law explained:

“And obviously, we have a tool that does exactly that. It actually helps you find the most commonly cited domains.  …if you put in whatever niche you’re interested in, you can see not only the top domains that get mentioned most often across all of the thousands, hundreds of thousands, millions of conversations we have indexed. You can also see the individual pages that get most commonly mentioned.

Obviously, if you can get your brand on those pages, yeah, immediately your AI visibility is going to shoot up in a pretty dramatic way.”

Citations Are The New Backlinks

Tim Soulo called citations the new backlinks for the AI search era and recommended their Brand Radar tool for identifying where to get mentions. In my opinion, getting a brand mentioned anywhere that’s relevant to your users or customers could also be helpful for ranking in the regular search  as well as AI (Read: Google’s Branded Search Patent)

Watch the Ahrefs podcast starting at about the 6:30 minute mark:

How to Win in AI Search (Real Data, No Hype)

Google Discusses Digital PR Impact On AI Recommendations via @sejournal, @martinibuster

Google’s VP of Product for Google Search confirmed that PR activities may be helpful for ranking better in certain contexts and offered an explanation of how AI search works and what content creators should focus on to stay relevant to users.

PR Helps Sites Get Recommended By AI

Something interesting that was said in the podcast was that it could be beneficial to be mentioned by other sites if you want your site to be recommended by AI. Robby Stein didn’t say that this is a ranking factor. He said this in the context of showing how AI search works, saying that the behavior of AI is similar to how a human might research a question.

The context of Robby Stein’s answer was about what businesses should focus on to rank better in AI chat.

Stein’s answer implies the context of the query fan-out technique, where, to answer a question, it performs Google searches (“questions it issues“).

Here’s his answer:

“Yeah, interestingly, the AI thinks a lot like a person would in terms of the kinds of questions it issues. And so if you’re a business and you’re mentioned in top business lists or from a public article that lots of people end up finding, those kinds of things become useful for the AI to find.”

The podcast host, Marina Mogilko, interrupted his answer to remark that this is about investing in PR. And Robby Stein agreed.

He continued:

“So it’s not really different from what you would do in that regard. I think ultimately, how else are you going to decide what business to go to? Well, you’d want to understand that.”

So the point he’s making is that in order to understand if a business should be recommended, the AI, like a human, would search on Google to see what businesses are recommended by other sites. The podcast host connected that statement to PR and Stein agreed. This aligns with anecdotal experiences where not just Google’s AI but also ChatGPT will provide answers to recommendation type queries with links to sites that recommend businesses. As the podcast host suggested and Stein seems to agree, this raises the importance of PR work, getting sites to mention your business.

Mogilko then noted that her friends might not have seen the articles that were published as a result of PR activities but that she notices that the AI does see those mentions and that the AI uses them in answers.

Robby agreed with her, affirming her observation, saying:

“That’s actually a good way of thinking about it because the way I mentioned before how our AI models work, they’re issuing these Google searches as a tool.”

Content Best Practices Are Key To Ranking In AI

Stein continued his answer, shifting the topic over to what kind of content ranks well in an AI model. He said that the same best practices for making helpful and clear content also applies for ranking in AI.

Stein continued his answer:

“And so in the same way that you would optimize your website and think about how I make helpful, clear information for people? People search for a certain topic, my website’s really helpful for that. Think of an AI doing that search now. And then knowing for that query, here are the best websites given that question.

That’s now… will come into the context window of the model. And so when it renders a response and provides all of these links for you to go deeper, that website’s more likely to show up.

And so it’s a lot of that standard best practices around building great content really do apply in the AI age for sure.”

The takeaway here is that helpful and clear content is important for standard search, AI answers, and people.

The podcast host next asked Robby about reviews, candidly remarking that some people pay for reviews and asking how that would “affect the system.” Stein didn’t address the question about how paid reviews would affect AI answers, but he did circle back to affirming that AI behaves like a human might, implying that if you’re going to think about how the AI system approaches answering a question, think of it in terms of how a human could go about it.

Stein answered:

“It’s hard. I mean, the reviews, I think, again, it’s kind of like a person where like imagine something is scanning for information and trying to find things that are helpful. So it’s possible that if you have reviews that are helpful, it could come up.

But I think it’s tricky to say to pinpoint any one thing like that. I think ultimately it’s about these general best practices where you want is reliable. Kind of like if you were to Google something, what pages would show up at the top of that query? It’s still a good way of thinking about it.”

AI Visibility Overlaps With SEO

At this point, the host responded to Stein’s answer by asking if optimizing for AI is “basically the same as SEO?”

Stein answered that there’s an overlap with SEO, but that the questions are different between regular organic search and AI. The implication is that organic search tends to have keyword-based queries, and AI is conversational.

Here’s Stein’s answer:

“I think there’s a lot of overlap. I think maybe one added nuance is that the kinds of questions that people ask AI are increasingly complicated and they tend to be in different spaces.

…And so if you think about what people use AI for, a lot of it is how to for complicated things or for purchase decisions or for advice about life things.

So people who are creating content in those areas, like if I were them, I would be a student of understanding the use cases of AI and what are growing in those use cases.

And there’s been some studies that have done around how people use these products in AI.

Those are really interesting to understand.”

Stein advised content creators to study how people are using AI to find answers to specific questions. He seemed to put some emphasis on this, so it appears to be something important to pay attention to.

Understand How People Use AI

This next part changes direction to emphasize that search is transforming beyond just simple text search, saying that it is going multimodal. A modality is a computer science word that refers to a type of information such as text, images, speech, or video. This circles back to studying how users are interacting with AI, in this case expanding to include the modality of information.

The podcast host asked the natural follow-up question to what Stein previously said about the overlap with SEO, asking how business owners can understand what people are looking for and whether Google Trends is useful for this.

Stein affirmed that Google Trends is useful for this purpose.

He responded:

“Google Trends is a really useful thing. I actually think people really underutilize that. Like we have real-time information around exactly what’s trending. You can see keyword values.

I think also, you know, the ads has a really fantastic estimation too. Like as you’re booking ads, you can see kind of traffic estimates for various things. So there’s Google has a lot of tools across ads, across the search console and search trends to get information about what people are searching for.

And I think that’s going to increasingly be more interesting as, a lot more of people’s time and attention goes towards not just the way people use search too, but in these areas that are growing quickly, particularly these long specific questions people ask and multimodal, where they’re asking with images or they’re using voice to have live conversation.”

Stein’s response reflects that SEOs and businesses may want to go beyond keyword-based research toward also understanding intent across multiple ways in which users interact with AI. We’re in a moment of volatility where it’s becoming important to recognize the context and purpose in how people search.

The two takeaways that I think are important are:

1. Long and specific questions
2. Multimodal contexts

What makes that important is that Stein confirmed that these kinds of searches are growing quickly. Businesses and SEOs should, therefore, be thinking, will my business or client show up if a person searches with voice using a lot of specific details? Will they show up if people use images to search? Image SEO may be becoming increasingly important as more people transition to finding things using AI.

Google Wants To Provide More Information

The host followed up by asking if Google would be providing more information about how users are searching, and Stein confirmed that in the future that’s something they want to do, not just for advertisers but for everyone who is impacted by AI search.

He answered:

“I think down the road we want to get, provide a glimpse into what people are searching for broadly. Yeah. Not just advertisers too. Yeah, it could be forever for anyone.

But ultimately, I think more and more people are searching in these new ways and so the systems need to better reflect those over time.”

Watch the interview at about the 13:30 minute mark:

Featured Image by Shutterstock/Krot_Studio