Q&A: Answer Engine Optimization (AEO) Course: Get Traffic From AI

Every insight links to its original source.

Ahrefs' complete AEO course teaches how AI search works, how to run brand gap and keyword research, create content AI cites, earn brand mentions, optimize YouTube, and measure AI visibility.

Jun 17, 2026AhrefsYouTube

What is Answer Engine Optimization (AEO) and how does it differ from traditional SEO?
AEO is the practice of making content visible and useful to AI systems that deliver direct answers, such as Google AI Overviews, ChatGPT, Perplexity, Gemini, and Copilot. In traditional SEO you compete for positions in a list of results, while in AEO the AI synthesizes dozens of sources and you compete for a mention or citation.
The practice is also called GEO (generative engine optimization) or LLMO (large language model optimization), and it builds on top of SEO rather than replacing it: quality content, authority, and technical health still matter as the foundation.
Why should businesses pay attention to AEO right now?
AI search is shrinking traditional clicks while producing exceptionally high-converting traffic: an AI overview on Google reduces the click-through rate of the number one ranking page by 58%, and in June 2025 AI search drove 12.1% of Ahrefs' sign-ups despite accounting for only 0.5% of traffic.
The broader momentum is significant as well: ChatGPT has roughly 900 million weekly users, handles about 12% of Google search volume, and AI traffic to websites has grown 9.7 times in the past year.
Where do AI search engines get their information?
AI systems draw on two sources: static training data, which is a snapshot of the internet updated roughly every six months, and real-time retrieval (RAG), where the AI fetches and reads fresh web pages when a question is too specific or recent for training data. This means brands can be baked into training data through widespread mentions or be picked up through real-time search, and existing SEO skills influence the latter.
Because training data is static, a recently launched product is unknown to the model until retrieval or retraining; ranking in Google, earning backlinks, and creating quality content directly affect whether AI picks up pages during real-time retrieval.
What is query fan-out and why does it matter?
Query fan-out is the technique where one prompt is expanded into many long-tail sub-queries run simultaneously behind the scenes; research cited in the course found the average prompt triggers 9 to 11 fan-out queries, with some as high as 28. To be included in AI answers, content needs to be relevant across an entire topic rather than optimized for a single keyword.
Fan-out queries are synthetic, inconsistent, and over 95% have zero search volume, so they should be treated as a window into which topics AI considers important rather than a new keyword list to optimize for; the AI responses report in Ahrefs Brand Radar makes them visible for ChatGPT and Perplexity prompts.
Do all AI search platforms cite the same sources?
No. Of the top 50 most cited domains across Google AI Overviews, ChatGPT, and Perplexity, only seven appeared on all three platforms, a 14% overlap. Google AI Overviews favor authoritative sites plus YouTube and Reddit, ChatGPT leans toward high-authority publishers (median domain rating 90), and Perplexity aligns most closely with traditional Google search.
Even Google's own products diverge: AI Overviews and AI Mode share only a 13.7% citation overlap despite being 86% semantically similar, and AI Mode's top cited domain is YouTube, with heavier use of Quora and social platforms.
For prioritization, Google's AI features and ChatGPT account for the vast majority of AI search traffic, while brands already ranking well in Google have a natural head start with AI Overviews and Perplexity.
What does winning AI visibility actually look like?
AI visibility comes in three forms: cited and linked, mentioned but not linked, and not visible at all. Only about 28% of AI mentions include a link, varying from 51.6% on Perplexity down to 10.7% on AI Overviews, so unlinked brand mentions are a major part of the game.
Unlinked mentions still matter because they feed LLM training associations, and in a study of 75,000 brands, branded web mentions had the strongest correlation with AI Overviews visibility at 0.664, stronger than backlinks, domain rating, or referring domains.
Citations are relatively rare but tend to occur on high-traffic queries; on Perplexity links appear in about 78% of total impressions despite being in only 51% of individual mentions.
What content characteristics make pages more likely to be cited by AI?
Content length is nearly irrelevant (correlation of 0.04 with citations, and 53.4% of cited pages are under 1,000 words), while freshness and format matter a lot: AI-cited content is about 25.7% fresher than traditional results, and 43.8% of cited pages across AI Overviews and ChatGPT are listicles. Writing should lead with the answer (BLUF), keep sections self-contained, use entity-rich and simple declarative language, and stay fresh through meaningful updates.
On ChatGPT specifically, 89.7% of its top cited pages were updated in 2025 and 76% were refreshed within the last 30 days, making refreshing declining 'sleeper' pages with existing backlinks one of the fastest ways to gain AI visibility.
Labeling original frameworks with your brand name and distributing them widely prevents LLMs from absorbing the idea as generic knowledge without crediting you.
Which technical issues can silently block AI visibility?
The main blockers are robots.txt rules disallowing AI crawlers such as GPTBot, ClaudeBot, and Google-Extended (around 5.9% of 140 million sites block GPTBot), JavaScript-rendered content that crawlers like ChatGPT's cannot see, and slow pages that get dropped during real-time retrieval. Cloudflare's default robots.txt AI-bot-blocking feature can also flag content as off-limits without the site owner realizing it.
Other checks include clean HTML heading structure, schema markup (evidence of direct AEO benefit is mixed but it does not hurt), and optimizing for hallucinated URLs, since AI assistants send visitors to 404 pages 2.87 times more often than Google Search does.
The llms.txt proposed standard is not officially supported by any major LLM provider, so robots.txt remains the file that matters most right now.
How can businesses measure AI visibility when much of the data is hidden?
Three tracking pillars work together: AI referral traffic (via a custom GA4 channel group or Ahrefs Web Analytics), AI bot activity on the site (via server logs or Ahrefs Bot Analytics with Cloudflare), and self-reported attribution from a 'How did you hear about us?' question. Referral data is an undercount because some platforms strip referrer information, and many AI-driven conversions surface only as direct or branded-search traffic.
At Ahrefs, around 3% of conversions over the last year came from AI based on self-reported data, and AI visitors converted at a much higher rate than organic search visitors, which would have gone unnoticed without asking directly.
Referral behavior varies by platform: ChatGPT's search-result links pass referrer data but in-content links on paid accounts do not, Claude tracks properly, Perplexity and Copilot track on web but not in their apps, and Grok passes no referral data at all.
Is investing in AEO worth it given how small AI traffic volumes still are?
By raw volume it is small: AI referral traffic averages about 0.25% of a site's total traffic, and Google sends roughly 210 times more traffic than the top AI platforms combined. However, the traffic quality is exceptional because AI recommendations arrive pre-qualified, with Ahrefs' AI visitors converting at 23 times the rate of organic search visitors, Vercel seeing 10% conversion rates from AI traffic, and Tally crediting AI as its largest acquisition channel.
The course also frames much of AEO's value as brand awareness inside the AI conversation rather than clicks: most AI impressions lead to later branded searches or social engagement, and AEO is treated as a new layer on top of SEO rather than an alternative to it.
A key caveat acknowledged is that AI visibility is not perfectly measurable, and AI platforms differ in resistance to misinformation; in a planted fake-brand test, Gemini and Perplexity repeated the misinformation in 37 to 39% of answers while ChatGPT stayed under 7%.

Q&A: Answer Engine Optimization (AEO) Course: Get Traffic From AI

Every insight links to its original source.

Ahrefs' complete AEO course teaches how AI search works, how to run brand gap and keyword research, create content AI cites, earn brand mentions, optimize YouTube, and measure AI visibility.

Jun 17, 2026AhrefsYouTube

What is Answer Engine Optimization (AEO) and how does it differ from traditional SEO?
AEO is the practice of making content visible and useful to AI systems that deliver direct answers, such as Google AI Overviews, ChatGPT, Perplexity, Gemini, and Copilot. In traditional SEO you compete for positions in a list of results, while in AEO the AI synthesizes dozens of sources and you compete for a mention or citation.
The practice is also called GEO (generative engine optimization) or LLMO (large language model optimization), and it builds on top of SEO rather than replacing it: quality content, authority, and technical health still matter as the foundation.
Why should businesses pay attention to AEO right now?
AI search is shrinking traditional clicks while producing exceptionally high-converting traffic: an AI overview on Google reduces the click-through rate of the number one ranking page by 58%, and in June 2025 AI search drove 12.1% of Ahrefs' sign-ups despite accounting for only 0.5% of traffic.
The broader momentum is significant as well: ChatGPT has roughly 900 million weekly users, handles about 12% of Google search volume, and AI traffic to websites has grown 9.7 times in the past year.
Where do AI search engines get their information?
AI systems draw on two sources: static training data, which is a snapshot of the internet updated roughly every six months, and real-time retrieval (RAG), where the AI fetches and reads fresh web pages when a question is too specific or recent for training data. This means brands can be baked into training data through widespread mentions or be picked up through real-time search, and existing SEO skills influence the latter.
Because training data is static, a recently launched product is unknown to the model until retrieval or retraining; ranking in Google, earning backlinks, and creating quality content directly affect whether AI picks up pages during real-time retrieval.
What is query fan-out and why does it matter?
Query fan-out is the technique where one prompt is expanded into many long-tail sub-queries run simultaneously behind the scenes; research cited in the course found the average prompt triggers 9 to 11 fan-out queries, with some as high as 28. To be included in AI answers, content needs to be relevant across an entire topic rather than optimized for a single keyword.
Fan-out queries are synthetic, inconsistent, and over 95% have zero search volume, so they should be treated as a window into which topics AI considers important rather than a new keyword list to optimize for; the AI responses report in Ahrefs Brand Radar makes them visible for ChatGPT and Perplexity prompts.
Do all AI search platforms cite the same sources?
No. Of the top 50 most cited domains across Google AI Overviews, ChatGPT, and Perplexity, only seven appeared on all three platforms, a 14% overlap. Google AI Overviews favor authoritative sites plus YouTube and Reddit, ChatGPT leans toward high-authority publishers (median domain rating 90), and Perplexity aligns most closely with traditional Google search.
Even Google's own products diverge: AI Overviews and AI Mode share only a 13.7% citation overlap despite being 86% semantically similar, and AI Mode's top cited domain is YouTube, with heavier use of Quora and social platforms.
For prioritization, Google's AI features and ChatGPT account for the vast majority of AI search traffic, while brands already ranking well in Google have a natural head start with AI Overviews and Perplexity.
What does winning AI visibility actually look like?
AI visibility comes in three forms: cited and linked, mentioned but not linked, and not visible at all. Only about 28% of AI mentions include a link, varying from 51.6% on Perplexity down to 10.7% on AI Overviews, so unlinked brand mentions are a major part of the game.
Unlinked mentions still matter because they feed LLM training associations, and in a study of 75,000 brands, branded web mentions had the strongest correlation with AI Overviews visibility at 0.664, stronger than backlinks, domain rating, or referring domains.
Citations are relatively rare but tend to occur on high-traffic queries; on Perplexity links appear in about 78% of total impressions despite being in only 51% of individual mentions.
What content characteristics make pages more likely to be cited by AI?
Content length is nearly irrelevant (correlation of 0.04 with citations, and 53.4% of cited pages are under 1,000 words), while freshness and format matter a lot: AI-cited content is about 25.7% fresher than traditional results, and 43.8% of cited pages across AI Overviews and ChatGPT are listicles. Writing should lead with the answer (BLUF), keep sections self-contained, use entity-rich and simple declarative language, and stay fresh through meaningful updates.
On ChatGPT specifically, 89.7% of its top cited pages were updated in 2025 and 76% were refreshed within the last 30 days, making refreshing declining 'sleeper' pages with existing backlinks one of the fastest ways to gain AI visibility.
Labeling original frameworks with your brand name and distributing them widely prevents LLMs from absorbing the idea as generic knowledge without crediting you.
Which technical issues can silently block AI visibility?
The main blockers are robots.txt rules disallowing AI crawlers such as GPTBot, ClaudeBot, and Google-Extended (around 5.9% of 140 million sites block GPTBot), JavaScript-rendered content that crawlers like ChatGPT's cannot see, and slow pages that get dropped during real-time retrieval. Cloudflare's default robots.txt AI-bot-blocking feature can also flag content as off-limits without the site owner realizing it.
Other checks include clean HTML heading structure, schema markup (evidence of direct AEO benefit is mixed but it does not hurt), and optimizing for hallucinated URLs, since AI assistants send visitors to 404 pages 2.87 times more often than Google Search does.
The llms.txt proposed standard is not officially supported by any major LLM provider, so robots.txt remains the file that matters most right now.
How can businesses measure AI visibility when much of the data is hidden?
Three tracking pillars work together: AI referral traffic (via a custom GA4 channel group or Ahrefs Web Analytics), AI bot activity on the site (via server logs or Ahrefs Bot Analytics with Cloudflare), and self-reported attribution from a 'How did you hear about us?' question. Referral data is an undercount because some platforms strip referrer information, and many AI-driven conversions surface only as direct or branded-search traffic.
At Ahrefs, around 3% of conversions over the last year came from AI based on self-reported data, and AI visitors converted at a much higher rate than organic search visitors, which would have gone unnoticed without asking directly.
Referral behavior varies by platform: ChatGPT's search-result links pass referrer data but in-content links on paid accounts do not, Claude tracks properly, Perplexity and Copilot track on web but not in their apps, and Grok passes no referral data at all.
Is investing in AEO worth it given how small AI traffic volumes still are?
By raw volume it is small: AI referral traffic averages about 0.25% of a site's total traffic, and Google sends roughly 210 times more traffic than the top AI platforms combined. However, the traffic quality is exceptional because AI recommendations arrive pre-qualified, with Ahrefs' AI visitors converting at 23 times the rate of organic search visitors, Vercel seeing 10% conversion rates from AI traffic, and Tally crediting AI as its largest acquisition channel.
The course also frames much of AEO's value as brand awareness inside the AI conversation rather than clicks: most AI impressions lead to later branded searches or social engagement, and AEO is treated as a new layer on top of SEO rather than an alternative to it.
A key caveat acknowledged is that AI visibility is not perfectly measurable, and AI platforms differ in resistance to misinformation; in a planted fake-brand test, Gemini and Perplexity repeated the misinformation in 37 to 39% of answers while ChatGPT stayed under 7%.

Q&A: Answer Engine Optimization (AEO) Course: Get Traffic From AI

What is Answer Engine Optimization (AEO) and how does it differ from traditional SEO?

Why should businesses pay attention to AEO right now?

Where do AI search engines get their information?

What is query fan-out and why does it matter?

Do all AI search platforms cite the same sources?

What does winning AI visibility actually look like?

What content characteristics make pages more likely to be cited by AI?

Which technical issues can silently block AI visibility?

How can businesses measure AI visibility when much of the data is hidden?

Is investing in AEO worth it given how small AI traffic volumes still are?

Q&A: AI Search Optimization Guide: Brand Mentions in LLMs

How to Improve AI Visibility in ChatGPT via Competitor Analysis

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How to Do SEO in the AI Era: Beginner's Guide to Google and AI Search

Q&A: Answer Engine Optimization (AEO) Course: Get Traffic From AI

What is Answer Engine Optimization (AEO) and how does it differ from traditional SEO?

Why should businesses pay attention to AEO right now?

Where do AI search engines get their information?

What is query fan-out and why does it matter?

Do all AI search platforms cite the same sources?

What does winning AI visibility actually look like?

What content characteristics make pages more likely to be cited by AI?

Which technical issues can silently block AI visibility?

How can businesses measure AI visibility when much of the data is hidden?

Is investing in AEO worth it given how small AI traffic volumes still are?

Q&A: AI Search Optimization Guide: Brand Mentions in LLMs

How to Improve AI Visibility in ChatGPT via Competitor Analysis

Q&A: GEO vs SEO: Is AI Visibility Just SEO With a New Label?

How to Do SEO in the AI Era: Beginner's Guide to Google and AI Search