This report distills real insights from 60+ scaling businesses across 15 industries. Based on client engagements, discovery calls, and hands-on implementations, it reveals the six operational problems that block growth, what they actually cost founders, and how companies that systemize early unlock 30–40% productivity gains within 90 days.
Answer Engine Optimization (AEO): The Complete Guide to Getting Cited by ChatGPT, Gemini, and Perplexity
- February 24, 2026
12 mins read
Table of Contents
Your Quick-Start Checklist
- Run the free HubSpot AEO Grader to see where you stand today.
- List your top 50 to 100 target questions using the seven question patterns, search data, competitor research, and sales call transcripts.
- Pick an affordable answer tracking tool and start monitoring your share of voice.
- Restructure your most important pages using the guide, problem-solution, or comparison template with question-based headers, chunked sections, and answer-first architecture.
- Move your help center to a subdirectory and add internal cross-linking.
- Create a real Reddit account and start providing genuine value in relevant subreddits.
- Record one to three YouTube videos targeting high-value questions where few videos currently exist.
- Ungate your most important content or create public versions that LLM crawlers can access.
- Publish your pricing, FAQs, and detailed product information publicly.
- Build a smart 404 page to capture traffic from hallucinated links.
- Run a controlled experiment with 100 test questions and 100 control questions.
- Measure results after two to four weeks and double down on what works.
What Is Answer Engine Optimization (AEO)?
Answer Engine Optimization – AEO for short – is how you get AI-powered tools like ChatGPT, Google Gemini, Perplexity, and Claude to mention, cite, and recommend your business when someone asks a question.
Think about it this way. When someone types “what’s the best project management tool for a 20-person agency” into ChatGPT, the answer it gives will name specific products. AEO is the work you do to make sure your product is one of those names.
You might also hear the term GEO, which stands for Generative Engine Optimization. Same concept, different label. AEO has become the more popular term because “answer” is more precise – we are talking specifically about showing up in text-based answers, not image generation or video creation.
How Is AEO Different From SEO?
This is where things get interesting because two of the sharpest minds in this space actually disagree on how different AEO really is.
Where They Agree
Both Kipp Bodnar (CMO of HubSpot) and Ethan Smith (CEO of Graphite, who has been doing SEO since 2007) agree on the fundamentals. SEO is the foundation for AEO. The things you have been doing for traditional search – quality content, good site structure, answering user questions – all of that still matters and gives you a head start.
Where They Disagree
Kipp Bodnar’s position is that AEO is meaningfully different from SEO and should be treated as its own discipline. His evidence:
Almost 60% of AI citations do not come from the top 20 Google results. If SEO and AEO were basically the same thing, that number would be much lower.
Ethan Smith’s position is that the differences are real but overstated.
He thinks a lot of the “AEO is completely new” narrative is driven by people selling AEO-specific tools. In his view, the core technology is similar – the main differences are in citation optimization, the head of search working differently, and the long tail being much larger.
Both perspectives have merit, and honestly, which one matters more probably depends on your specific business. If you are already strong in SEO, Smith’s view suggests you are closer to AEO success than you think. If you are not ranking well in Google but have strong brand presence elsewhere, Bodnar’s view suggests AEO might be your chance to leapfrog competitors.
The Practical Differences That Actually Matter
In SEO, you win by ranking. In AEO, you win by being mentioned: Getting the number one citation in ChatGPT’s answer does not guarantee you will be the recommended product. The LLM pulls from many sources and the brand that gets mentioned most frequently across those sources tends to appear first. This is fundamentally different from Google where position one gets the most clicks regardless.
SEO is built on keywords. AEO is built on entities: Google’s algorithm matches keywords on your page to keywords in the search query. LLMs are trying to understand what your company actually is and does as a whole entity. They need a coherent picture of your brand, not just a collection of keyword-optimized pages.
Google queries are short. LLM prompts are long: The average Google search is four to six words. The average LLM prompt is 25 to 60 words – sometimes much more. Someone might type “I run a 15-person digital marketing agency in New York, we currently use Monday.com but it is too expensive, what are the best alternatives that integrate with Slack and have good reporting.” That level of specificity creates a massive long tail of questions that never existed in Google search.
SEO uses networks of pages. AEO uses single chunked pages: In the SEO world, you might create eight separate pages about subtopics of CRM integration, all linking back to a main pillar page. In AEO, you consolidate everything into one comprehensive page broken into clearly labeled chunks. Each chunk is a self-contained answer that the LLM can pull independently based on the specific prompt it receives.
Months vs. hours. Building SEO authority is a six to twelve month process. HubSpot has gotten ChatGPT traffic within hours of publishing timely content. This speed difference changes the entire playbook for how quickly you can test and iterate.
One platform vs. many platforms: SEO is overwhelmingly about Google. AEO spans ChatGPT, Gemini, Perplexity, Claude, Grok, and others – each with different data sources and citation preferences.
Domain authority does not matter in AEO – yet: In Google, a site with high domain authority has a massive advantage. In AEO, what matters is how authoritative AI perceives your brand on a specific topic based on reviews, Reddit discussions, affiliate mentions, and whether your information is consistent with what other credible sources say. This is actually great news for smaller businesses and startups.
How Do AI Answer Engines Actually Work?
Understanding the mechanics helps you optimize smarter, so here is the simplified version. There are two systems working together inside every LLM that generates answers with citations.
The core model is the base AI that was trained on massive datasets. When you ask “what is the capital of Bangladesh” and it instantly says “Dhaka”, that answer comes from the core model’s training data. You cannot easily influence this. Changes to training data might show up a year later, and the effort required to influence it is enormous and not worth pursuing for most businesses.
RAG – Retrieval-Augmented Generation is the part you can actually optimize for. When someone asks a question that needs current or specific information, the LLM runs a real-time web search, retrieves relevant pages (citations), and then summarizes those pages into an answer. This is where AEO happens. Your job is to make sure your content appears in those retrieved citations and that the information the LLM pulls from your content is accurate, useful, and positions your brand well.
Here is the key insight that makes all of this click: 85% of ChatGPT citations come from only two to three domains per answer. Your goal is to become one of those domains for the questions that matter to your business.
Almost everything in this guide is about optimizing for the RAG pipeline, not the core model.
In the AEO world, there are two distinct ways your brand can appear in an AI answer, and they require slightly different strategies.
Mentions happen when the AI names your company or product in its response text. “For project management, popular options include Asana, Monday.com, and ClickUp” – that is a mention for all three brands. What you care about with mentions is whether you are being mentioned at all, whether the mention is positive, and how you stack up against competitors in the same answer.
Citations happen when the AI includes a clickable link to your content as a source. This is what drives actual traffic to your site. What you care about with citations is which specific pages are being linked, and whether those pages are optimized for visitors who are highly qualified and ready to take action.
HubSpot found that people arriving through AI citations are so ready to convert that they had to rethink their landing pages. The standard marketing funnel approach – educate, nurture, then convert – does not apply when someone has already had a 10-message conversation with ChatGPT about exactly what they need. These visitors need a fast path to action, not more education.
The Question Patterns That LLMs Actually Cite
Before we get into the step-by-step playbook, there is something worth understanding about how LLMs decide what to pull from and what to ignore. They are trained on question-and-answer pairs, which means content structured around specific question patterns gets picked up far more reliably than content that just covers a topic in general terms.
Here are the patterns that consistently show up in AI-generated answers:
“What is [concept] and how does it work?”
Instead of targeting a keyword like “marketing strategy,” reframe it as “What is a marketing strategy and how does it work?” This aligns with exactly how LLMs generate responses – they are looking for content that directly mirrors the question someone asked.
“Best [solution] for [specific use case].”
Queries like “best CRM for small businesses” or “best project management tools for remote teams” are comparison queries, and LLMs answer these constantly. Being the definitive source for a “best of” list in your niche is one of the fastest ways to get cited repeatedly.
“How to choose [solution type].”
Decision-making content is extremely valuable to AI engines serving business users. “How to choose a marketing automation platform” or “how to choose the right business systemization consultant” – these are high-intent questions where people are ready to take action.
“[Tool A] vs [Tool B] comparison.”
Comparison content is citation gold. When someone asks an LLM “what is the difference between ClickUp and Monday.com,” it pulls from pages that directly compare them. If you have written that comparison with real experience and honest pros and cons, you become the go-to citation.
“[Industry] + [process] + best practices.”
Queries like “SaaS onboarding best practices” or “construction company project management best practices” trigger industry-specific answers. If you have created content for a specific vertical, you can own these answers because most competitors write generic content.
“What does [term] mean in [context]?”
Definition queries are incredibly frequent in LLMs. If your page includes inline definitions – “churn rate in SaaS refers to the percentage of customers who cancel their subscription in a given period” – that exact sentence can get pulled into an AI answer.
“[Current year] guide to [topic].”
Freshness matters. LLMs prioritize content that is clearly current. A page titled “2026 guide to business automation” will be preferred over an undated guide covering the same material.
The practical takeaway here is simple: take your five most important topics and rewrite your page headers using these patterns. Then make sure the first one to two sentences after each header directly answer the question. That combination – question-pattern header plus immediate answer – is what gets extracted and cited.
The AEO Playbook: Step-by-Step
Step 1: Figure Out Which Questions You Want to Win
Start with what you already know. Pull your existing search data – your paid search terms, your competitor’s paid search terms, and your highest-performing keywords. Then transform those keywords into natural language questions using the patterns above. You can literally paste a list of keywords into ChatGPT and ask “turn these into questions someone would ask an AI assistant” and it does a solid job.
But do not stop there. The real gold in AEO is the long tail – questions so specific they were never searched on Google. To find these, mine your sales call transcripts, customer support tickets, Reddit threads, and community forums. What are people actually asking when they are trying to solve problems related to your product?
A few tools that help with question research: AlsoAsked.com surfaces question clusters around any topic. AnswerThePublic finds “what,” “how,” and “why” questions in your niche. Google’s People Also Ask sections give you real questions people are searching. And honestly, just reading through Reddit threads and Quora questions in your industry for 30 minutes will give you more long-tail questions than any tool.
For example, if you run a business operations consultancy, someone is not going to Google “ClickUp consultant.” But they might ask ChatGPT “I’m running a construction company with 45 employees, we’re using spreadsheets for everything and it’s falling apart, what project management system should I use and should I hire someone to set it up?” That is the kind of ultra-specific question where you can win if you have the right content.
Step 2: Set Up Answer Tracking
You need to know where you stand before you can improve. Get an AEO tracking tool and start monitoring how often you appear in AI answers for your target questions.
Answer tracking is trickier than keyword tracking because LLM answers are not static. Ask the same question three times and you might get three different answers. Ask a slightly different version of the same question and you will get different citations. Ask on ChatGPT versus Perplexity and the results will vary significantly.
What you are tracking is your share of voice – the percentage of time your brand appears across multiple runs of the same question across multiple platforms. Think of it like a batting average, not a single at-bat.
Step 3: Study Who Is Currently Getting Cited
Before you start creating content, study what is already working. For your target questions, look at who is showing up as citations right now. You will typically see citations falling into these groups:
Company websites and product pages, YouTube and Vimeo videos, Reddit and Quora threads, major affiliate publishers like Dot Dash Meredith (the company behind Good Housekeeping, Investopedia, and AllRecipes – probably the most cited publisher in LLMs), smaller affiliate blogs and review sites, and industry-specific publications like TechRadar for B2B tech.
Map out which groups dominate the citations for your target questions. That tells you where you need to build presence. If Reddit threads are showing up in 70% of the citations for your key questions and you have zero Reddit presence, that is your biggest gap.
Step 4: Restructure Your On-Site Content
This is where the rubber meets the road. Your website content needs to be rebuilt around what I call answer-first architecture. But it helps to have a framework rather than starting from scratch every time.
Every page you create for AEO should work on three levels simultaneously. First, it needs to be structured for direct answers – that is the AEO layer. Clear question-based headers, concise answers in the first sentence or two, inline definitions of key terms, and proper heading hierarchy so bots can parse it cleanly. Second, it needs to be formatted for citations – that is about including statistics with source attribution, named entities, outbound links to credible sources, and clean structure that makes extraction easy. Third, it should be designed for engagement – modular sections people can actually use, related questions that lead deeper, and content that works whether someone reads the whole page or just one chunk.
Depending on what you are writing, one of three page structures tends to work best.
The comprehensive guide structure works when you want to own an entire topic. Start with “What is [topic]?” and give a direct definition. Follow with “Why [topic] matters” backed by statistics and named sources. Then “How to implement [topic]” with step-by-step process. Then “[Topic] vs alternatives” with honest comparison. Close with frequently asked questions. This structure is how you become the Wikipedia of your niche for a specific topic – and Wikipedia is one of the most cited sources by LLMs precisely because of this comprehensive, neutral, well-structured approach.
The problem-solution structure works for content aimed at people actively trying to fix something. Open with “The problem: why [issue] happens” with clear definition and real statistics. Then “The solution: [your approach]” with implementation steps and expected outcomes. Follow with a case study showing specific metrics, timeline, and lessons. Close with tools and resources. This one is especially effective because it mirrors how people actually describe problems in LLM prompts.
The comparison structure works when people are evaluating options. Lead with a quick comparison table. Then give each option its own section covering features, pricing, and ideal use case. Close with “Which should you choose?” using a decision framework with use case scenarios. Comparison content gets cited at an outsized rate because “X vs Y” is one of the most common question patterns in LLMs.
Whichever structure you use, there is a checklist of things every page should include: question-based headlines, direct answers in the first one to two sentences, FAQ schema markup, inline definitions for key terms, proper H2 and H3 hierarchy, statistics with source attribution, named entities and expert references, outbound links to authoritative sources, and summary sections that can be extracted independently.
Answer all the follow-up questions someone might ask about your topic. The more subtopics your page addresses, the more prompts it is relevant for. And if you answer a question that nobody else has answered, you become the only possible citation for that query. That is an incredibly powerful position.
Step 5: Turn Your Help Center Into an AEO Asset
This is Ethan Smith’s sleeper recommendation and I think it is brilliant. People ask LLMs incredibly specific product questions – “does ClickUp integrate with Xero,” “can I set up automated time tracking for contractors in Monday.com,” “which CRM has a native WhatsApp integration.”
These questions are often answerable by help center articles, but most companies have never thought about their help center as a discovery channel.
Three things to fix immediately. First, move your help center from a subdomain (help.yoursite.com) to a subdirectory (yoursite.com/help). Subdirectories consistently perform better for search visibility. Second, add strong internal cross-linking between help center pages so bots can crawl the full knowledge base. Third, fill in the long tail – create articles for those obscure-but-real use cases that come up in sales calls and support tickets. Even consider opening a community forum where customers ask and answer questions, which naturally expands your help center content into territory no competitor covers.
Step 6: Build Your Off-Site Citation Presence
Your own website is only one piece of the puzzle. LLMs pull from many sources, and the brand that appears across the most citations wins.
Reddit is arguably the single most important off-site channel for AEO right now. OpenAI has a paid data partnership with Reddit, so Reddit content is heavily weighted in ChatGPT answers. But here is the thing – you cannot game Reddit. Fake accounts get banned. Automated comment spam gets deleted. The Reddit community is exceptionally good at policing itself.
What actually works is embarrassingly simple. Create a real account. Find threads that are relevant to citations you want to show up in. Identify yourself – say your name and where you work. Then provide a genuinely useful answer. The format that works best is straightforward: acknowledge the question, share what you have seen work in your actual experience, give two or three specific actionable points, mention the key takeaway, and if you have written something more detailed on the topic, link to it naturally at the end. No hard pitch. Just value with a breadcrumb.
Webflow has a few team members who do exactly this and it works well for them. HubSpot maintains their own subreddit and encourages customers to participate. You do not need 10,000 comments. Even five authentic, helpful comments in the right threads can move the needle.
The data-rich comment is what gets cited the most. A Reddit comment that says “in my experience working with 40+ small businesses on ClickUp implementations, the three most common mistakes are…” with specific numbers and real examples – that is exactly the kind of content LLMs pull into answers about ClickUp implementation. Generic opinions get ignored. Specific, experience-based answers with real data get cited.
Quora works on similar principles but with a slightly different format. The answers that perform best on Quora lead with credentials (“I’ve been working in business systemization for 8 years and have helped 50+ companies scale their operations”), give a direct short answer, then break down the detail with specific points and examples, and close with a bottom-line summary. Again, specific beats generic. A Quora answer citing your own data – “based on our work with 39 companies, the average time savings from systemization was…” – has a much higher chance of being cited than a generalized response.
YouTube and Vimeo are a massive opportunity, especially for B2B. Think about it – there are millions of cooking videos and travel vlogs, but almost nobody is making videos about “how to set up automated financial reporting for a construction company in ClickUp.” If you make that video, you might be the only citation available when someone asks that question in ChatGPT. There is no community moderation to worry about either. You make a video, it exists, and it can be cited.
Affiliate and review sites like Forbes Advisor, G2, Capterra, TechRadar, and Software Advice do influence LLM answers. Kipp Bodnar mentioned there is a noticeable correlation between paid placements on these sites and how you show up in AI answers. This lever is expensive but controllable. If you have the budget, it is the most predictable way to increase your citation presence.
Cross-platform syndication is the multiplier. When you publish a detailed guide on your site, create a Reddit post summarizing the key insights, answer related Quora questions with excerpts and links, share the takeaways on LinkedIn, and discuss it in relevant Twitter threads. This creates multiple citation sources for the same underlying expertise, which increases the odds that at least one of them gets picked up when an LLM is assembling its answer.
Build your own community. A branded subreddit, a community forum on your site, or even an active Discord server creates ongoing organic content about your brand that LLMs can discover and cite.
Step 7: Run Real Experiments
Here is something both experts were adamant about: most AEO advice you read online is unverified. People say things, other people repeat them, and suddenly it is “best practice” even though nobody actually tested it.
Ethan Smith’s background is in academic research and he applies that rigor here. Take 200 target questions. Split them into a control group (100 questions you do nothing about) and a test group (100 questions where you apply your intervention). Track both groups for a couple of weeks before making any changes to establish a baseline. Then apply your intervention to the test group only – maybe you post Reddit comments, create YouTube videos, or restructure on-page content.
If your test group improves and your control group stays flat, your intervention worked. But do not stop there. Reproduce the experiment. Do it again with a different set of questions. If it works three times, you have something real. If it only worked once, it might have been coincidence.
This discipline will save you enormous amounts of wasted effort. In both SEO and AEO, most work produces zero results. Experimentation tells you which work actually matters.
Step 8: Assemble the Right Team
Your SEO team or agency should own on-site optimization and answer tracking – the skills transfer well. But off-site citation work – YouTube production, Reddit community engagement, affiliate outreach – requires different skills. You likely need a community-minded generalist marketer or content creator for that side of things. Most SEO specialists are not natural YouTube creators or Reddit participants, and that is fine. Just make sure you have both skill sets covered.
AEO Strategy by Business Type
B2B SaaS
The citations that dominate B2B answers are different from consumer queries – expect TechRadar, G2, industry-specific blogs, and comparison articles. Most B2B answers in LLMs are not clickable, meaning you cannot track impact through standard referral analytics. You need answer tracking tools combined with post-conversion surveys. Also recognize that B2B purchases involve dozens of touchpoints, so AEO influence often shows up as branded Google searches or direct traffic that is hard to attribute.
E-Commerce
LLMs are starting to show shoppable cards with product images and prices for commerce queries. Schema markup and rich snippets matter here. Number of reviews matters. You can track conversions more directly through last-touch referral data.
Early-Stage Startups
This is where AEO gets really exciting. Traditional SEO advice for startups is “wait until you have funding and domain authority.” AEO flips that completely. A brand new YC company can show up in ChatGPT answers tomorrow if people are talking about them on Reddit, if they get mentioned on a blog, or if they make a YouTube video answering a question nobody else has answered. For early-stage companies, the recommendation is simple: focus exclusively on citation optimization and long-tail questions. Skip the traditional SEO playbook entirely.
Local Businesses and Marketplaces
LLMs are rapidly incorporating maps and local search results. Citations come from Yelp, TripAdvisor, Google Maps, and local directories. Clickable local results are already showing up in LLM interfaces and this will only grow.
AEO Tools Worth Knowing About
Free
HubSpot AEO Grader – Scores your brand across OpenAI, Perplexity, and Gemini on five dimensions: brand recognition, market positioning, presence quality, sentiment, and share of voice. Gives you a competitive comparison and actionable recommendations. This is the right first step for any business.
Paid
HubSpot (via their Xfunnel acquisition) – Enterprise-grade AEO tracking, coming soon as part of HubSpot’s marketing platform.
Limy.ai – AEO tracking and optimization, specifically recommended by Kipp Bodnar.
Graphite.io – Full-service SEO and AEO agency with proprietary answer tracking technology. They also maintain a public directory of over 60 answer tracking tools.
Otterly.ai, Profound, Peec AI, Seer Interactive – Various AEO monitoring platforms worth evaluating.
Question Research Tools
AlsoAsked.com – Discovers question-based clusters around any topic. Great for finding the follow-up questions you should be answering on your pages.
AnswerThePublic.com – Finds “what,” “how,” and “why” questions in your niche.
Semrush Keyword Magic Tool – Filter for informational keywords and transform them into question patterns.
A Word of Caution on Tooling
Ethan Smith made a sharp observation here: AEO tooling is currently overpriced relative to what it does. Answer tracking is fundamentally a commodity – it tells you whether you show up for a question or not. There is no premium version of that information. His advice is to pick the cheapest tool that meets your needs and not get seduced by expensive platforms offering essentially the same data with a nicer dashboard. This mirrors what happened with SEO keyword tracking tools – there are hundreds of them and they all report the same rankings.
What About Gated Content?
If your best content is locked behind an email gate and LLM crawlers cannot access it, that content simply does not exist in the AEO world. It will never be cited.
The practical solution is to let LLM bots crawl your content while keeping the email gate for human visitors at the point of transaction. Most CMS platforms allow you to serve different experiences to bots versus humans.
But Kipp Bodnar made a bigger point that I think is worth sitting with: in the age of AI, all information is effectively public whether you choose to share it or not. Someone can use ChatGPT’s deep research mode to piece together your pricing from Reddit threads, Glassdoor reviews, and scattered blog mentions. If you do not control the narrative by making that information publicly available, people will find it anyway – and possibly in ways you do not want.
Pricing pages, FAQs, detailed product specifications, integration documentation – all of this should be public and accessible to LLM crawlers. The businesses that hoard information are not protecting themselves. They are just making it harder for AI to recommend them.
Does Writing Content With AI Hurt Your AEO?
Short answer: yes, if the content is 100% AI-generated with no human involvement.
Graphite ran a detailed study on this. They analyzed thousands of Google search results and ChatGPT citations alongside an AI detection tool. The finding was clear – only 10 to 12 percent of content being cited by LLMs is AI-generated. The other 88 to 90 percent is human-written or meaningfully human-edited.
But here is the nuance that matters. AI-assisted content – where a human uses AI to draft, outline, and organize, then layers in their own expertise, data, and perspective – works perfectly well. The problem is not using AI in your workflow. The problem is publishing AI output without adding anything original.
Pure AI content fails because it is derivative by design. A language model generates text by predicting the most statistically likely next word based on everything it has been trained on. The output is essentially an average of existing content. It does not contain new information, original research, or firsthand experience. LLMs and search engines are getting better at recognizing this, and content that offers nothing new gets filtered out.
There is also a structural reason why fully automated AI content cannot work long-term. If it did work, everyone would do it. Then LLMs would be summarizing AI-generated content that was itself summarizing AI-generated content – an infinite loop of derivatives. Researchers have studied this phenomenon and call it “model collapse.” The diversity of perspectives shrinks until you get a single consensus opinion on everything, which is the opposite of useful. Both Google and the LLM providers have strong incentives to prevent this, and they are actively working on it.
How Do AI Systems Detect Human-Written vs. AI-Written Content?
This is a question I hear constantly, and the answer is more nuanced than most people realize.
Graphite tested the accuracy of AI detection tools rigorously. They used Surfer SEO’s AI detector and validated it in two ways. First, they generated thousands of articles with AI and the detector correctly identified them at a very high rate. Second – and this is the clever part – they pulled 100,000 random URLs from Common Crawl that were published before ChatGPT existed. Since those articles were necessarily written by humans, any that got flagged as AI-generated were false positives. The false positive rate was only about 8 percent, meaning the detector is roughly 92 percent accurate.
How does detection actually work at a technical level? AI-generated text has measurable statistical properties that differ from human writing. AI content tends to be “typical” – it clusters around the statistical center of language patterns because the model is literally optimizing for the most probable next word. Human writing is messier, more varied, and more likely to contain unusual phrasing, unexpected tangents, and information that does not exist in the training data.
But text analysis is only part of the picture. Google and LLM providers also look at signals beyond the words themselves. They examine whether the content adds new information beyond what already exists on similar pages. They look at publishing patterns – a site that suddenly outputs 500 articles in a single week with consistent formatting and vocabulary raises flags. They assess whether the content source has demonstrated real expertise on the topic through other signals like reviews, backlinks, and social proof.
And this is not just algorithms doing the work. Both Google and the major LLM providers have human evaluation teams who review content quality and tune the systems to deprioritize automated spam. There are real people at ChatGPT deciding which citation sources to trust and which to downweight.
How to Create AI-Assisted Content That Will Not Get Filtered Out
The goal here is not to fool detection systems. That is a losing game and the wrong mindset. The goal is to create content that genuinely deserves to be cited because it offers things that pure AI output cannot.
Here is what I have found works, both from these expert conversations and from practical experience creating content for businesses.
Start with your own data: Nothing beats original numbers. If you ran a client project that reduced their operational costs by 30 percent, say that. If you surveyed 50 business owners about their biggest workflow bottleneck, share those results. AI cannot fabricate proprietary data and LLMs actively seek out novel statistics to cite. Original research – industry surveys, benchmarking studies, case study analysis, trend reports – is one of the strongest citation magnets you can build because by definition it cannot be found anywhere else.
Write from firsthand experience: “In our experience working with electrical contractors on ClickUp implementations, the number one mistake is trying to digitize their existing paper process instead of rethinking the workflow from scratch.” That sentence contains real expertise that no AI could generate because it comes from actually doing the work. Sprinkle these throughout your content.
Reference your specific frameworks and methodologies: If you have developed a proprietary approach – a named system, a specific sequence of steps you follow, a diagnostic framework – describe it. This is information gain that is unique to you and makes your content the only possible source for that particular knowledge.
Use AI for the scaffolding, not the substance: Let AI help you create an outline, suggest section headers, clean up awkward sentences, and organize your thoughts. Then go section by section and replace every generic statement with something specific from your own knowledge. The structure can come from AI. The insights should come from you.
Replace generic examples with real ones: AI loves to write things like “for example, a mid-sized company might use this approach to improve efficiency.” That is filler. Replace it with “when we implemented this for a childcare services company in the UK, their team went from spending 12 hours a week on manual reporting to getting automated dashboards every morning.” Specific beats generic every time.
Include perspectives that challenge the mainstream: AI-generated content is consensus content by definition – it reflects the average opinion. If you have a contrarian view or a nuanced take that most people miss, include it prominently. “Most consultants will tell you to automate first. I disagree – you need to systemize first and automate later, because automating a broken process just creates broken automation faster.” That kind of perspective is exactly what LLMs look for when they want to provide a comprehensive answer.
Edit until it sounds like you: Read every paragraph out loud. If it sounds like it could have been written by anyone, rewrite it. Your content should have a recognizable voice – your quirks, your preferred metaphors, your way of explaining things. AI text has a telltale blandness that both human readers and detection systems can spot.
Publish at human pace: Two well-crafted articles per week will outperform 20 AI-generated articles in both SEO and AEO. The volume signals alone can trigger scrutiny – and more importantly, you simply cannot add genuine original insight to 20 articles a week. Quality compounds. Quantity without quality does not.
Cross-check your facts against the consensus: LLMs verify claims by looking for consistency across multiple sources. If you state something that contradicts what five other credible sites say, the LLM will consider your content less trustworthy. Align on facts, then differentiate on perspective, analysis, and recommendations.
Apply the “only I could write this” test: Before you hit publish, ask yourself honestly: could someone have generated this exact content by spending five minutes with ChatGPT? If the answer is yes, the content needs more of you in it. If the answer is no – because it contains your data, your client stories, your frameworks, and your hard-won opinions – then you have created something worth citing.
Letting LLMs Index Your Content - Should You Allow It?
Yes. This is not really optional anymore.
Ethan Smith put it bluntly:
“It’s not your choice whether to play the game. You are playing the game whether you want to or not.”
If you block LLM indexing entirely, your competitors show up instead and you get nothing.
What you can do is allow indexing while blocking training. LLM providers use different bot user agents for crawling/indexing versus training their models. You can configure your robots.txt file to say “you can index my content for search results, but you cannot use it to train your model.” This gives you visibility in AI answers without contributing to the training data.
Webflow is actually building a feature to make this toggle easy for their users. Until that kind of tooling is standard, you can manually configure robots.txt to block training bots while allowing index bots.
Platform-by-Platform Breakdown
Each LLM has its own quirks when it comes to which sources it trusts and cites. Here is what the data shows:
ChatGPT leans heavily on Reddit because of OpenAI’s paid data partnership. Citation overlap with Google search results is surprisingly low – only about 35 percent. This means ChatGPT is surfacing a lot of content that would not appear on the first two pages of Google.
Perplexity is more aligned with traditional search. Citation overlap with Google is around 70 percent. If you are already ranking well in Google, you are more likely to show up in Perplexity.
Google Gemini is integrated with Google’s search infrastructure and AI Overviews. Being included in an AI Overview gives your organic listing a 30 to 35 percent higher click-through rate.
Claude relies less on Reddit compared to ChatGPT. Other web sources carry more relative weight.
Grok pulls primarily from X (Twitter) data. If your brand has a strong X presence, you will see disproportionate visibility in Grok but that advantage does not transfer to other platforms.
The practical takeaway: On-page optimization works similarly across all platforms. Off-page citation strategy is where you need to think platform by platform. Track your share of voice across at least two or three major platforms rather than putting all your eggs in one basket. As Ethan Smith pointed out, we do not know which platforms will win long-term. In 1999, nobody knew Google would beat AOL and Yahoo. Hedging is smart.
The Metrics That Matter in AEO
Share of voice – What percentage of the time does your brand appear when someone asks your target questions? Track this across multiple platforms and multiple runs of the same question.
Mention sentiment – Are AI answers talking about you positively, negatively, or neutrally? This is a brand marketing metric being applied to search, which tells you something about how different this world is from traditional SEO.
Citation frequency – How often do your specific URLs appear as clickable sources?
Conversion rate from LLM traffic – Track this separately from Google traffic. The difference will probably surprise you.
Brand recognition score – How accurately can AI platforms describe your company, products, and positioning? If you ask ChatGPT “what does [your company] do?” and the answer is wrong or vague, you have entity work to do.
Post-conversion attribution – Especially for B2B, ask new customers how they heard about you. A lot of LLM-influenced traffic shows up as direct visits or branded searches because people see your name in ChatGPT, open a new tab, and type your URL directly. Your analytics will call that “direct traffic” when it was actually AEO doing the work.
Mistakes That Will Cost You
Ignoring your 404 page: LLMs hallucinate URLs. They will generate links to pages on your site that do not exist. HubSpot had 300,000 hits to non-existent pages in a single month from hallucinated links. Build a smart 404 page that either redirects to relevant content or – if you want to get creative – dynamically generates a page based on what the hallucinated URL suggests the visitor was looking for.
Hard-gating all your content: If LLM crawlers cannot see it, it cannot be cited. Period.
Building your entire presence on social media: LinkedIn, X, and Instagram are walled gardens. Most LLMs cannot access that content deeply. A brand that is massive on LinkedIn but has nothing on the open web will underperform badly in AEO.
Spamming Reddit: Fake accounts get banned. Automated comments get deleted. The Reddit community catches this stuff fast and the backlash can actually hurt your brand sentiment, which is the opposite of what you want.
Assuming your Google rankings translate to AEO visibility: 60 percent of AI citations come from outside the top 20 Google results. Being number one in Google does not mean ChatGPT will recommend you.
Overpaying for AEO tools: Answer tracking is a commodity. The cheapest tool that reports your share of voice gives you the same information as the most expensive one. Do not get swept up in the hype.
Publishing pure AI-generated content: Both Google and LLMs are actively filtering it out. The content that gets cited has human expertise, original data, and real perspective baked in.
How to Audit Your Business for AEO and Convert It Into an Execution Checklist
So now you understand what AEO is, how it works, and what the experts recommend. The obvious next question is – where does my business actually stand right now, and what should I do first?
Run Your Audit
Go to HubSpot’s free AEO Grader. Enter your company name, country, industry, and niche. Download the full PDF report. This tells you how AI answer engines currently see your business, who your competitors are in AI answers, and where your gaps are.
Turn the Audit Into an Action Plan
Upload the PDF to ChatGPT or Claude with this prompt:
Prompt:“I just ran the HubSpot AEO Grader for my company. The PDF report is attached.Analyze the report and build me a prioritized AEO execution checklist. Specifically:
- Analyze my scores across ChatGPT, Perplexity, and Gemini. Tell me which platform is strongest, weakest, and why.
- Identify my biggest gaps in brand recognition, market score, presence quality, sentiment, and share of voice.
- Who are my main competitors in AI answers and what is my share of voice versus theirs?
- Is the AI confusing my brand with similarly named companies?
- Which sources talk about me positively and which am I completely missing from?
- Give me a prioritized checklist broken into: this week, this month, and ongoing. Be specific – tell me exactly what pages to create, platforms to get on, content to produce, and communities to participate in.
- What are the three highest-impact things I should do tomorrow morning?”
Additional context: [Add 2-3 sentences about what your company does, who you serve, and where you are most active online.]
Track Progress Monthly
Run the grader again every 30 days. Upload both reports and ask the AI to compare them – which scores improved, which stayed flat, and what to adjust. This turns a one-time audit into an ongoing feedback loop that tells you exactly what is working and what is not.
Everything we covered earlier in this guide – the content chunking, Reddit strategy, YouTube videos, entity optimization – that is the playbook. This audit process tells you which parts of the playbook to prioritize based on where your specific business is weakest.
Pro tip: Run this process for a client and you instantly have an AEO strategy proposal – the audit is the diagnostic, the checklist is the scope of work, and the 30-day recheck is the reporting framework.
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