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.
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Featured Image by Shutterstock/Nithid
