Generative Engine Optimization (GEO) – also known as Answer Engine Optimization (AEO) – is an emerging counterpart to traditional SEO, focused on making your content visible to AI-driven answer engines. Unlike classic search engines that list ten blue links, answer engines (like ChatGPT, Bing Chat, or Google’s Search Generative Experience (SGE)) provide direct answers. This article breaks down the mechanics of how large language models (LLMs) rank and retrieve content, and offers practical how-tos for optimizing your content for these AI-based searches.
Understanding How Answer Engines Work
Example of Google’s Search Generative Experience (SGE) providing an AI-generated snapshot above standard search results.
Before diving into optimization tactics, it’s crucial to understand where answer engines get their information. At a high level, generative AI search tools draw on two main data sources:
- Training Data: The knowledge base used to train the LLM (e.g., ChatGPT’s model). This includes vast swaths of internet text up to a certain cutoff date. An answer drawn from training data is generated from what the model “remembers” without doing a live lookup.
- Live Internet Data: Real-time information retrieved from the web. Many answer engines augment LLM responses with up-to-date data via a search index (for example, Bing or Google’s index). This is often called retrieval-augmented generation (RAG) – the model performs a web search and then integrates the results into its answer.
Some AI search engines use one source or the other, while hybrid systems use both:
- Pure LLM (Training Data–Only): Models like GPT-4 (without plugins) or Anthropic’s Claude rely solely on their internal training. They can answer based on knowledge up to their cutoff (e.g., September 2021 for many models) but won’t have current info.
- Retrieval-Augmented: Tools like Bing Chat or Perplexity.ai run a web search and feed relevant pages to the LLM. These feel more like traditional search – in fact, using ChatGPT with the browsing plugin or Bing integration produces results similar to Bing’s own search result. Google’s AI Overviews (the generative answers in SGE) similarly pull from live indexed pages.
- Hybrid Models: Emerging systems like Google’s upcoming Gemini aim to decide in real-time whether to answer from training data or perform a search. In other words, the AI might sometimes just “know” the answer and other times fetch fresh info.
Understanding this is key: if an answer engine is using live data, traditional SEO tactics (like ranking high in search) directly influence AI visibility. If it’s using training data, the game shifts to ensuring your information was well-represented in the material the model was trained on (which can be trickier).
Data Source Examples: ChatGPT and many AI assistants use Bing as their internet search source, while Google’s SGE (now AI Overviews) obviously uses Google’s index. For instance:
Table: Where various answer engines retrieve live data when they use it (as of late 2024).
Notice that many rely on Bing – a reflection of partnerships (Microsoft invested $14B in OpenAI) and the maturity of Bing’s index. This means Bing SEO is now as important as Google SEO if you want to appear in AI answers. For example, one study by Seer found content cited by ChatGPT’s search-enhanced mode overlapped significantly (73%) with Bing’s top results.
How LLMs “Rank” Content in Answers
Traditional search engines use algorithms (PageRank, etc.) to rank results. LLM-based answer engines don’t exactly “rank webpages” in the same way – instead, they select and synthesize content. Here’s how the process typically works under the hood:
- User Query Understood: The LLM interprets the user’s question. Because it’s trained on language patterns, it determines the intent and key terms. For example, ask “What’s the best CRM for a small business?” – it knows you want a recommendation of a CRM software suitable for small businesses.
- Retrieval (if enabled): If the system uses live data, it triggers a search with that query (or a modified query). The search engine returns top results just as it would for a normal search. Those results might be ranked by traditional means (keywords, links, etc.). For instance, Bing might return a PCMag article “10 Best CRMs for Small Businesses” and a Zapier blog “Best CRM Tools…”.
- Content Extraction: The answer engine’s algorithms pull snippets of content from those top pages. It might grab the list of CRM names from PCMag, and a definition from Zapier. If multiple sources agree or complement each other, that content is given more weight. It often helps if the content is in a simple format (like a list of “Best X for Y”) because it’s easy to excerpt.
- Synthesis by the LLM: The LLM takes those pieces (plus any relevant info from its own training data memory) and generates a consolidated answer in natural language. It tries to be coherent and answer the question fully. In our CRM example, ChatGPT might say: “For a small business, top CRM options include HubSpot (user-friendly and free for basic use), Salesforce (highly customizable, though more complex), and Zoho CRM (affordable with robust features)…”.
- Citations/Attribution: Some answer engines provide citations for transparency. Bing Chat and Google’s AI Overviews will cite the sources they used – often with hyperlinks. (ChatGPT in default mode usually doesn’t cite, but if using plugins or browsing, it might list sources or the user can ask for them.)
So, in effect, the “ranking” in an answer engine is a mix of traditional search ranking (to decide which sources to draw from) and the LLM’s own judgment of what information is relevant and credible to include. This is why continuing to do SEO (to rank well on search indices) remains foundational for GEO. In fact, early research confirms that *strong Google rankings correlate with frequent mentions in LLM answers. Brands that appear on page 1 of Google had a high correlation (~0.65) with being mentioned by GPT-4 in a large-scale test, whereas lower-ranked brands were mentioned far less.
However, there’s a twist: LLMs don’t just mindlessly echo top search results. They might ignore certain types of content:
- Forums, Social Media, or UGC: If the query is seeking a solution, LLMs tend to skip sources like Reddit or Quora in their final answer (even if those rank in search), because the AI knows those are discussions, not authoritative answers. For example, ask an LLM “What’s the best credit card for students?” – it won’t answer “Reddit” even if a Reddit thread ranks, it will pull from a bank site or a review article.
- Thin or Spammy Content: Modern AIs have some ability to detect low-quality or repetitive text. If a top result is deemed not useful or too ad-heavy, the LLM might not use it. (This isn’t foolproof, but it’s a factor.)
- Paywalled or Uncrawlable Content: If a site can’t be accessed by the crawler (e.g., behind a login or blocked by robots.txt), it won’t be used in a retrieved answer. Similarly, if your site explicitly disallows AI crawlers, they should avoid it (more on that later in the series).
In training-data-only answers, “ranking” is even more abstract. The LLM basically generates an answer from its neural network memory, influenced by how frequently and prominently information appeared during training. If your brand or content was mentioned across many high-quality articles, the model is more likely to “remember” it. In that sense, training data visibility is about prominence and consistency in the source material. For instance, if numerous reputable articles (that the AI trained on) say “Café Bustelo is a top coffee brand,” the AI is more likely to mention Café Bustelo when asked about best coffees. If your brand was rarely mentioned or only in obscure corners of the internet, an LLM might not even know about it.
Key Differences: Traditional SEO vs GEO Mechanics
Let’s highlight how GEO mechanics diverge from classic SEO:
- No Click-Through Stage: In SEO, even if you rank #1, the user chooses whether to click your link. With GEO, the AI might present information from your site without a click. The visibility is in the answer itself. This means your content’s phrasing might get used by the AI immediately. Optimizing for GEO often means writing in a way that an AI can lift and repurpose your content effectively (concise, factual sentences).
- Answer Composition: Traditional Google results could show multiple brands/sites – the user scans snippets. In an AI answer, information from multiple sources is merged. So you’re competing to be included in the composite answer, not just to be the top link. Sometimes several brands get named (e.g., “ChatGPT, which project management tools are best?” might list 3-5 options). Sometimes only one source is heavily used. Ensuring your content contains key answer elements (definitions, pros/cons, rankings, etc.) increases your chances of inclusion.
- Influence of Training Data: Past SEO didn’t consider “Did an AI read my site in its training?” But now, if an LLM like GPT-4 was trained on data as of 2021, an article you wrote in 2020 could still influence answers today even if your site isn’t currently top-ranked. It’s a strange retroactive effect – call it “historical SEO.” Conversely, brand-new content might not influence an offline model until it’s retrained. (Some answer engines mitigate this by always using retrieval for anything time-sensitive.)
- Dynamic Personalization: LLMs can tailor answers to the query context or user. For example, if the user says “Explain like I’m 5” or if the AI has a profile of the user’s preferences, the answer may adjust which facts to highlight. Traditional search results don’t rewrite themselves per user (beyond localization). This means GEO must consider multiple contexts – your content might be used in a straightforward answer or in a simplified explanation. Having content that can serve various angles (basic explanations, advanced details, use-case specific info) can help the AI find the right snippet for the right context.
- Update Pace: Google and Bing continuously crawl and re-rank pages. LLM training data updates much less frequently. For instance, if OpenAI doesn’t retrain its model for 6+ months, any new brand info won’t be reflected in ChatGPT’s base model answers in that period. However, those platforms may provide update mechanisms (plugins, live search). From an optimization standpoint, this means recent content might take time to pay off in AI answers unless the AI is explicitly fetching live data.
In summary, SEO and GEO are closely intertwined – strong traditional SEO lays the groundwork (ensuring you’re in the pool of sources an AI might draw from), while GEO adds an extra layer of considerations about how AI synthesizes answers. You influence AI, you optimize for search engines – meaning we can’t directly control AI outputs, but we can influence them by optimizing what the AI has to work with.
How-To: Optimizing Content for Generative Search
With the mechanics in mind, let’s get practical. How can you increase the likelihood that an AI will include your brand or content in its answer?
1. Ensure Crawlability by AI Bots
Just as you’d never block Googlebot from your site if you want SEO traffic, you shouldn’t block AI crawlers. Check your robots.txt for any disallow that might inadvertently block “GPTBot” (OpenAI’s crawler) or others. In mid-2023, OpenAI introduced the GPTBot user-agent and said content it’s allowed to crawl may be used to improve future models.
Also consider adopting the emerging llms.txt standard. Proposed in late 2024, the llms.txt file is a way to feed a curated list of your important content to language model. Think of it as a sitemap specifically for AI. By placing key facts and URLs in llms.txt, you “handhold” the AI to understand your site. Early experiments show this can boost an AI’s factual accuracy and completeness about your content. For example, an experiment with a company’s knowledge base found that answers with an llms.txt pointer were more accurate and on-topic, because the AI had a “cheat sheet” of the company’s information.
Action item: Create an llms.txt at your domain’s root with links to high-value pages (product FAQs, about us, spec sheets, knowledge base articles) and key facts about your brand. It’s not yet a widespread standard, but it’s easy to implement and could give you an edge if LLMs start using it routinely.
2. Structure Content for Easy Digestion
AI models excel at parsing well-structured content. Use clear headings, bullet points, and concise paragraphs. A model looking for an answer will more likely grab text from a section that’s clearly labeled and formatted. For instance, if your blog post has a section “How to Choose a VPN – Key Factors” with a nice bullet list of factors, an AI can directly quote or summarize that list for a user asking “What should I consider when choosing a VPN?”.
Lists and Tables: Content presented in list form often gets priority in answers. Google’s SGE frequently presents answers as bullet points or step-by-steps. ChatGPT, when sourcing info, loves to enumerate points if they were in the source. If you have “Top 5 reasons…” or a comparison table, those tend to be excerpted. In one example, I Heart Naptime’s recipe page was cited by ChatGPT because it had clearly delineated sections (“Ingredients”, “Steps”, “Tips”) and bullet points – perfect for snippet extraction.
Direct Q&A: Incorporate Q&A style content on your pages (which is also good for traditional featured snippets). If you have an FAQ that literally asks and answers the exact question a user might ask the AI, you stand a good chance of being used. For example, an e-commerce site might have an FAQ: “What is the warranty on your products?” – if a user asks the AI the same, the model might pull the answer directly from that FAQ.
3. Focus on Authoritative, Well-Cited Content
Large language models are trained to prefer content that sounds authoritative. They have an inherent sense of what looks like a credible statement versus a flimsy one. This is similar to Google’s emphasis on E-E-A-T (Experience, Expertise, Authority, Trustworthiness). If your content includes evidence of expertise, an AI is more likely to use it confidently. Some trust signals to include:
- Citations and References: If you present a fact or statistic on your site, cite the source (with a link or at least a mention). An AI parsing your text sees that you provide sources, which may make it regard the info as reliable. Ironically, the AI’s answer itself might not show your citation to the user, but it will incorporate the info. Think of it this way: content that is well-cited is more likely to become the AI’s citation.
- Author Bylines and Bios: Content with a named author and bio (especially highlighting credentials) can influence AI. For example, an article “Marathon Training Tips” by Jane Doe, a certified running coach, signals expertise. An AI might mention “according to a running coach...” when using that info. This aligns with Google’s best practices and can carry over to AI.
- First-Hand Experience: Google added an extra E for “Experience” in E-E-A-T – content that shows personal experience. In AI context, if a user asks for recommendations or reviews, an answer that includes first-hand insight might be rated as more “human-like” and valuable. Brands can infuse this by including customer testimonials, case studies, or narrative elements in content. An AI might say “Users report that [Your Product] is particularly good for…”, reflecting such content.
- Awards and Certifications: Trust signals like awards, certifications, or endorsements can also improve an AI’s trust in the content. For instance, if your site says “Voted #1 by PCMag in 2024” or “ISO-certified security”, an AI might incorporate that fact or at least weigh your content as more credible in its internal reasoning.
Remember, LLMs want to give correct information. If your content is thorough and well-sourced, it reduces the AI’s risk of error by using it. In SEO terms, think of this as optimizing for “answer rank” – the likelihood your content is chosen as the basis of an answer.
4. Target Conversational Phrases and Questions
Keyword optimization isn’t dead in the AI era; it’s just adapted. You should still identify the natural language phrases people use when querying AI. Often these are longer and more conversational than typical Google searches. For example, a Google search might be “best budget smartphone 2025”, whereas a ChatGPT query might be, “I’m looking for a good budget-friendly smartphone for 2025, any recommendations?”.
Research user questions: Use forums, Reddit, Quora, and especially Google’s People Also Ask boxes to see how real people phrase questions. Many of those exact questions can be directly posed to an AI. Tools like AlsoAsked or AnswerThePublic (free or freemium) can give you a trove of common questions in your domain.
Then, incorporate those questions and their answers into your content. For example, if you sell project management software, have content that explicitly addresses queries like “What’s the best project management tool for a small team?” or “How does [Your Tool] compare to [Competitor]?”. In SEO, you might have reserved those for blog posts or FAQs; in GEO, it’s critical to meet the user’s actual query head-on in the content.
Also, include contextual specifics that users often add. AIs allow very specific queries (e.g., “best running shoes for flat feet in hot weather”). If you have niche content like “Best Running Shoes for Flat Feet – Summer Edition,” you’re more likely to match that long-tail question than a generic page would. Keep an eye on trends too (e.g., adding “2025” or “post-pandemic” in content if those are common in queries) since recency and context terms can influence whether an AI deems your content relevant.
5. Leverage Third-Party Platforms and Citations
Not all optimization happens on your own site. Often, being mentioned on other reputable sites is the way to get into AI answers. Think of this like off-page SEO meets PR:
- Get Featured in “Best of” Lists or Reviews: As noted earlier, ChatGPT’s recommendations for products or services often cite third-party review sites, not the brand’s own site. If you want to be in that answer, your product needs to be on those lists. This means doing outreach to be included in industry roundups, top 10 lists, etc. If direct inclusion is hard, consider offering expertise or data to the authors of those lists to get a mention.
- Contribute Thought Leadership: Write guest posts or secure interviews on authoritative industry blogs and media. If an LLM sees “According to [Industry Journal]…” and your brand is providing the insight, it adds to your credibility in its eyes. Even press releases or news features about your brand can help, since LLMs trained on news articles will have that knowledge (ChatGPT, for example, often references things reported in major news sources if relevant).
- Boost Ratings & Listings: For local businesses or those listed on platforms (TripAdvisor, G2, etc.), having a strong presence there can pay off. In local queries, ChatGPT might cite Tripadvisor or Yelp. In software queries, it might mention G2 or Capterra ratings. Ensure your profiles on those sites are robust and positive.
- Wikipedia & Wikidata: For factual queries (like company founding dates, CEO names, etc.), models often rely on Wikipedia content. If your brand has a Wikipedia page, keep it up to date and accurate (following Wikipedia’s guidelines, of course). Contribute to Wikidata as well for structured facts – these can influence what an AI “knows” about your entity.
In short, treat the whole information ecosystem as your playground. If your brand is absent from the places an AI looks, it won’t magically dream it up. In GEO, digital PR and classic SEO amplification are more important than ever for establishing your brand’s footprint across the web.
6. Monitor AI Mentions and Iterate
Treat GEO like an ongoing process. You’ll need to monitor how often and in what context your brand appears in AI outputs. This is challenging (since AI answers aren’t indexed like web pages), but some strategies include:
- Manual Testing: Regularly ask ChatGPT, Bing Chat, Google’s AI, etc., some of your priority questions and see what they say. Vary the phrasing and note if your brand comes up. Keep a log over time.
- Use Emerging Tools: New tools are launching for AI mention tracking. For example, SpyFu’s “SpyGPT” project asked ChatGPT 250M questions and created a searchable database of the answers. They let you search for your brand to see in what questions it appears. Tools like this can give you a bird’s-eye view of where you stand. (We’ll discuss more tools in a later article.)
- Set Up Alerts: If an AI cites sources, use Google Alerts or mention tracking for those source pages plus your brand. For instance, if an AI often mentions a particular blog in answers about your industry, follow that blog’s content – or set alerts for your brand name appearing on new pages (which might indicate an AI-influenced roundup has included you).
- User Feedback: Encourage your community or customers to tell you if they see your brand (or don’t see it) in AI answers. For example, a user asking an AI about your product vs competitors might share the answer with you – this is valuable intel.
When you discover how you’re being mentioned (or not mentioned), iterate your strategy:
- If you never show up for a category of question, analyze the answers: Who is being mentioned and why? Do they have certain content or partnerships you lack?
- If your brand is mentioned but with outdated or incorrect info, that’s a sign to update your content and possibly engage with the AI company if it’s a serious error.
- If a competitor is consistently chosen over you, examine their digital footprint. Do they rank higher? Do they have more press? Use that to inform your next moves (more content, better SEO, etc.).
7. Stay Educated on AI Search
Mechanics will evolve. Google’s AI search might change how it selects sources; OpenAI might introduce new tools or allow site owners more influence. For example, Google’s SGE was experimental but is now rolling out as a default AI Overview in 2022, and it will likely keep shifting in format and impact. Being aware of such changes lets you adjust proactively.
Follow reliable sources – AI research blogs, SEO experts sharing GEO experiments, and official announcements from Google/Bing/OpenAI. By staying in the loop, you’ll catch new optimization opportunities (or pitfalls) early. For instance, when Google introduced a policy of citing at least two sources in AI overviews, savvy content creators started ensuring they had supporting references from other sites (to encourage Google’s AI to pick up their point with a citation).
GEO is where SEO was two decades ago – fascinating, a bit mysterious, but rapidly maturing. By understanding how answer engines gather and generate responses, you position yourself to make strategic optimizations rather than stabs in the dark. The core takeaway is that good SEO fundamentals and high-quality content are prerequisites for GEO success, but they’re not the whole story. You must also think about how AI uses that content: is it easy to parse? Is it trusted and frequently referenced?
Generative AI isn’t a black box miracle; it follows data and patterns. By ensuring your brand is part of those data and patterns – through robust content, technical accessibility, and savvy off-site presence – you can make your voice heard in the answers of the future. As generative search usage explodes (ChatGPT was reportedly handling over 10 million queries a day by late 2024, surpassing Bing’s volume), the time to adapt your optimization strategy is now.