AI search optimization is the work of making your content easy for AI search features to find, understand and cite. Search engines now write answers as well as list links, so being the source behind the answer is a goal next to ranking. Most of it is good SEO plus a few new checks: crawler access, clear answers, a brand that is mentioned elsewhere, and new ways to measure results.
This guide covers what generative engine optimization (GEO) and answer engine optimization (AEO) mean, how Google, ChatGPT, Perplexity and Bing each find sources according to their own documentation, what to change on your site, how to measure it, and which popular tips have no official backing.
What AI search optimization, GEO and AEO mean
Three labels are used for the same job.
- AI search optimization is the plain-English umbrella term. It covers any work to improve how your content shows up in AI-powered search.
- Generative engine optimization (GEO) is the term from academic research. It focuses on visibility inside responses written by generative models.
- Answer engine optimization (AEO) is an older, broader label for being the source that answer boxes, voice assistants and AI answers pull from.
The terms are not standardized. Vendors and agencies define them slightly differently, so don’t expect a clean line between them. For a marketing team the practical question is simple: when someone asks an AI tool a question in your field, does it cite you or mention your brand?
Where the GEO term comes from
The term was introduced in a research paper, “GEO: Generative Engine Optimization,” by Pranjal Aggarwal, Vishvak Murahari, Tanmay Rajpurohit, Ashwin Kalyan, Karthik Narasimhan and Ameet Deshpande. It was first submitted to arXiv in November 2023, revised in June 2024, and published at KDD 2024. The authors propose GEO as a framework for helping content creators improve their visibility in generative engine responses, and they built a benchmark called GEO-bench to test it.
The abstract reports that GEO methods can boost visibility “by up to 40%” in generative engine responses, and that effectiveness varies by domain. Read that carefully. It is a result from the authors’ own benchmark, not a promise for your site, and the “up to” and the domain variation both matter. Use the paper as a starting point for ideas, not as a guarantee.
How it relates to SEO
Google’s own documentation is direct on this point. Its AI features page says there are no additional technical requirements to appear in AI Overviews or AI Mode, and that specific optimization isn’t required. Pages need to be indexed and eligible to be shown in Google Search with a snippet. Google’s newer guide to generative AI features adds one setting: a site must be included in Search generative AI features in Search Console to be eligible. That means the foundation of AI search optimization on Google is ordinary SEO.
That does not make the new work pointless. Other AI products have their own crawlers and their own rules, and the way you are measured is different. A useful split:
| Layer | What it covers | Same as SEO? |
|---|---|---|
| Access | Crawlers can fetch and index your pages | Yes, plus new bot names to manage |
| Content | Clear, original, well-structured answers | Mostly yes |
| Reputation | Your brand is described consistently across the web | Overlaps with links and PR |
| Measurement | Citations, mentions and referrals from AI surfaces | New |
If your SEO basics are weak, fix those first. The SEO tools category lists tools for audits, keyword research and rank tracking that help with that groundwork.
How each AI surface finds sources
Each product publishes different details. The sections below report only what the official documentation says, and say where nothing is published.
Google AI Overviews and AI Mode
Google’s documentation says AI Overviews and AI Mode show relevant links to help people explore a topic. Both may use a technique it calls “query fan-out,” which issues multiple related searches across subtopics and data sources while building a response. Google says this lets the features show a wider and more diverse set of helpful links than a classic search results page.
What that means for you:
- Eligibility is the same as Search. Your page has to be indexed and eligible to appear with a snippet. Google’s newer guide also says the site must be included in Search generative AI features in Search Console, so check that your site has not been excluded.
- No special files or markup. Google’s guide to generative AI features says you don’t need new machine-readable files, AI text files, markup or Markdown to appear, because Google Search itself doesn’t use them. It also says structured data isn’t required for generative AI search and there is no special schema.org markup to add.
- The standard best practices apply. Google lists allowing crawling, internal links, good page experience, text content, quality images and video, and structured data that matches the visible content.
- Controls are the usual ones. You can limit how your content appears using
nosnippet,data-nosnippet,max-snippetornoindex. - Query fan-out changes what to cover. Because one question can trigger several related searches, a page that covers the follow-up questions around a topic has more chances to be selected than a thin page about one narrow phrase. This is an inference from how Google describes the system, not a documented ranking rule.
ChatGPT search
OpenAI publishes a page describing its crawlers. Three matter here:
- OAI-SearchBot is used to surface websites in ChatGPT’s search features. OpenAI says sites that opt out of it will not be shown in ChatGPT search answers, though they can still appear as navigational links. To help ensure your site appears, OpenAI recommends allowing OAI-SearchBot in robots.txt and allowing requests from its published IP ranges. OpenAI notes it can take about 24 hours from a robots.txt update for its systems to adjust.
- GPTBot is used to collect content for training generative AI foundation models. Disallowing it signals that your content should not be used for training.
- ChatGPT-User is used for certain user actions in ChatGPT and Custom GPTs. OpenAI says robots.txt rules may not apply because a user initiates the action, and that ChatGPT-User is not used to determine whether content may appear in search. OpenAI points site owners to OAI-SearchBot in robots.txt for search opt-outs.
The key point is that search inclusion and training are controlled by different bots. You can block GPTBot and still allow OAI-SearchBot. OpenAI does not publish a ranking formula for which pages ChatGPT cites, so any guide claiming one is guessing.
If you want to understand how ChatGPT is changing as a marketing channel, including its ads product, see our ChatGPT ads guide.
Perplexity
Perplexity documents two bots:
- PerplexityBot is designed to surface and link websites in Perplexity search results. Perplexity states it is not used to crawl content for AI foundation models. Perplexity’s documentation says to allow PerplexityBot in robots.txt and to permit requests from its published IP ranges.
- Perplexity-User supports user actions: when someone asks a question, it may visit a page to help answer. Perplexity says it generally ignores robots.txt rules, because the user’s request triggers the fetch.
Perplexity publishes the IP ranges for both bots as JSON files and recommends allowing them in your web application firewall (WAF) together with user-agent verification. If your CDN or security tool blocks unknown bots by default, check that these are not caught.
Bing and Microsoft Copilot
Microsoft does not publish a source-selection formula for Copilot, so this guide does not describe one. What Microsoft does publish:
- Bing Webmaster Guidelines say Bing and Copilot search experiences rely on the same core crawling, indexing and ranking foundation as traditional search. They stress clear content, crawlability, sitemaps, structured data that matches visible content, and IndexNow, which lets you notify Bing when content is added, updated or removed.
- AI Performance in Bing Webmaster Tools is a report in public preview that shows how your content appears across Microsoft Copilot, AI-generated summaries in Bing, and select partner integrations. Microsoft described the launch as an early step toward GEO tooling. More on the metrics below.
The practical takeaway: make sure Bing can crawl and index you, submit a sitemap, consider IndexNow, and verify your site in Bing Webmaster Tools so you can see citations.
Comparison
| Surface | Source-finding bot or system | Controlled in robots.txt? | Official reporting for site owners |
|---|---|---|---|
| Google AI Overviews / AI Mode | Google Search index; query fan-out | Standard Search controls (nosnippet, noindex, etc.) | Search Console Performance report (Web) and Generative AI performance report (impressions) |
| ChatGPT search | OAI-SearchBot | Yes; separate from GPTBot (training) | No dedicated report published in the sources used here |
| Perplexity | PerplexityBot | Yes; Perplexity-User generally ignores it | No dedicated report published in the sources used here |
| Bing / Copilot | Bing index | Standard Bing controls | AI Performance in Bing Webmaster Tools (public preview) |
Crawler access and robots.txt
Access is the cheapest thing to check and the most common thing to get wrong.
What to check
- Read your robots.txt. Look for blanket
Disallow: /rules for unknown bots, and for rules aimed at specific AI crawlers that someone added without a clear reason. - Decide per purpose. Training bots and search bots are separate decisions. If you want citations in ChatGPT answers, OAI-SearchBot must be allowed. If you don’t want your content used for model training, you can disallow GPTBot. The same logic applies to PerplexityBot, which Perplexity says is not used for foundation model training.
- Check your firewall and CDN. OpenAI asks that you allow requests from its published IP ranges, and Perplexity publishes IP lists for its bots. A robots.txt that allows a bot does nothing if your WAF returns an error to it.
- Allow time for changes. OpenAI says robots.txt updates can take about 24 hours to be picked up.
- Don’t rely on robots.txt for secrecy. Google’s documentation says robots.txt is not a mechanism for keeping a page out of Google, and a disallowed page can still be indexed if other sites link to it. Use
noindexor password protection for that. Google also notes that crawlers are not obliged to follow robots.txt.
Example
A robots.txt that allows the search bots and blocks the training bot looks like this:
User-agent: OAI-SearchBot
Allow: /
User-agent: PerplexityBot
Allow: /
User-agent: GPTBot
Disallow: /Whether to block training bots is a business decision, not an SEO one. The point is that you can make it separately from the decision to be cited.
What about llms.txt?
llms.txt is a proposal, not a standard. It was authored by Jeremy Howard and first published in September 2024, and it suggests a markdown file at /llms.txt that gives language models a short guide to your site. The proposal’s own site says it is open to community input and describes thousands of sites publishing the file. Adoption by sites is not the same as a search engine using it as a ranking or sourcing signal.
For Google, the answer is documented: its guide to generative AI features lists llms.txt files among things you can ignore for Google Search, saying Google Search itself doesn’t use them. OpenAI’s and Perplexity’s crawler documentation describe their bots and robots.txt behavior, and we did not find llms.txt named as a source-selection signal there. If you run documentation that agents consume, publishing one is cheap and may help tools that read it. Don’t expect it to lift your visibility in search, and don’t pay anyone who sells it as a ranking trick.
Content practices that help
Google’s blog post on succeeding in AI search says to focus on unique, valuable content, provide a good page experience, make sure pages can be accessed, and make sure structured data matches the visible content. The practices below follow that guidance and the GEO research framing. They are good habits, not guaranteed levers.
Lead with a clear answer
AI systems quote and summarize passages. A page that states the answer in the first few lines under a question-style heading is easier to extract than one that circles the point for 400 words. For every target query:
- Put the direct answer in 1–3 sentences right after the heading.
- Follow with the detail: steps, examples, caveats.
- Use headings that match how people phrase the question.
This is also the format that earns traditional featured snippets, so the work pays off in both places.
Structure for scanning
Use descriptive headings, short paragraphs, lists for steps and tables for comparisons. Google lists text content and internal links among its basics. Clean structure also helps human readers, which is the point.
Cover the follow-up questions
Because Google describes query fan-out as issuing multiple related searches, topic coverage matters. For a pillar page, list the questions a buyer would ask next (cost, alternatives, limits, how to start) and answer each one, either on the page or on linked pages. Don’t overdo it: Google’s guide says creating separate content for every fan-out variation mainly to manipulate AI responses violates its scaled content abuse spam policy. Our AI marketing strategy guide is built this way: one hub, with related questions covered in depth.
Publish original data and first-hand material
Original research, your own pricing data, screenshots from your own accounts, and expert quotes are things a model cannot invent and a competitor cannot copy. This is the safest long-term bet because it gives people a reason to cite you. Don’t invent numbers. If you didn’t measure it, don’t claim you did.
Build entity and brand mentions
AI systems describe brands based on what they can find. If your company name, founders, product and category are described consistently on your site, in directories, and by third parties, it is easier for any system to connect them. Concretely:
- Use the same company and product names everywhere.
- Keep an About page with factual details: what you do, who runs it, where you operate.
- Earn mentions in reputable places in your industry, such as interviews, reviews, podcasts and partner pages.
- Keep profiles on major platforms accurate.
I could not find an official statement from Google, OpenAI or Perplexity that brand mentions are a ranking factor in their AI answers. Google’s guide goes the other way on one point: it says seeking inauthentic mentions across the web isn’t as helpful as it might seem. Treat this as sensible reputation work with a plausible benefit, not a documented rule.
Keep important pages fresh
Out-of-date pages (old prices, retired features, last year’s statistics) are a risk when an AI summarizes them. Review high-value pages on a schedule, update facts, and show the modified date. If you use Bing, IndexNow lets you tell it about updates quickly. Bing’s guidelines recommend keeping content accurate and up to date. The Google pages used here don’t publish a freshness ranking rule for the AI features, so the case for updating is accuracy and trust.
Use structured data honestly
Google says no special schema.org markup is needed for AI Overviews or AI Mode. Structured data is still worthwhile for the rich results it supports and for helping search engines understand a page, with one firm rule: Google’s policy says not to mark up content that is not visible to readers, and that markup should be a true representation of the page. Violations can lead to manual actions that remove rich result eligibility. Add schema that matches what is on the page, and skip the rest.
Make text available
Google lists text-based content in its best practices. Don’t hide key facts only inside images, videos or interactive widgets. Add a text summary or transcript beside them.
Page checklist
| Check | Why |
|---|---|
| Indexed and snippet-eligible | Google’s stated requirement |
| Direct answer under each question heading | Easy to extract |
| Related questions answered | Matches query fan-out |
| Original data or first-hand examples | Gives a reason to cite |
| Facts and dates current | Avoids outdated summaries |
| Structured data matches visible content | Google policy |
| Not blocked by robots.txt, WAF or login wall | Basic access |
Measuring AI visibility
Measurement is the least mature part of this field. Use official reports first, and treat third-party numbers as estimates.
Google Search Console
Google’s AI features page says sites appearing in AI Overviews and AI Mode are included in Search Console’s overall Performance report under the Web search type, so AI features are not split out there. In June 2026 Google added a separate Generative AI performance report. Its help page says it shows impressions from AI Overviews and AI Mode (not clicks), with views by page, country, device and date, and that it was rolled out to all websites as of August 31, 2026. Not every property may see it yet, and it may be missing if your site has too few impressions in these features or has been excluded from them. Use the regular Performance report for clicks and the new report for AI impressions, and track trends for the queries and pages you care about. See Google Search Console.
Google Analytics 4
Google Analytics Help says the Organic Search channel includes traffic from non-ad links in search results, including Google’s AI Overviews and AI Mode. So that traffic is not separated by default either.
Other AI products send visits too, and those may show up as referrals from the product’s domain. The default channel group can’t be edited, but Google Analytics lets you create custom channel groups. A practical setup is a custom channel group that matches referral sources for the AI products you care about (for example by source domain). Check your own Referral report to see which source names actually appear before writing rules. I did not find an official statement from OpenAI or Perplexity listing the referral parameters they use, so verify in your own data. Google Analytics is the tool to use for this.
Bing Webmaster Tools
AI Performance in Bing Webmaster Tools is the first-party report found in this research that shows citations rather than impressions. It is in public preview and reports:
- Total citations: how many times your content was displayed as a source in AI-generated answers in the period.
- Average cited pages: the mean number of unique pages shown as sources per day.
- Grounding queries: key phrases the AI used when retrieving content that was referenced in answers.
- Page-level citation activity: citation counts for specific URLs.
In June 2026 Microsoft announced four more preview capabilities: Intents (grounding queries grouped into categories such as informational or commercial), Topics (related grounding queries grouped into themes), Citation Share (your share of citations for a grounding query) and Compare (overlay a previous period). Microsoft says Bing’s data on grounding queries is a sample of overall citation activity.
It covers Microsoft Copilot, AI summaries in Bing and select partner integrations. The grounding queries are especially useful: they show how the AI turned a question into retrieval phrases, which you can use to improve your headings and coverage.
Server logs
Your logs show whether OAI-SearchBot, PerplexityBot and others are fetching your pages, using the user agent strings the vendors publish. A bot visit is not a citation, but no visits is a sign to check robots.txt and your firewall.
AI visibility tools
Third-party tools track whether a brand appears in AI answers by running prompts on a schedule. Some SEO suites include this. In this directory, the AI search visibility tag lists tools built for this job. SEO suites such as Semrush and Ahrefs may offer related features, so check each official site for current details.
Know the limits of these tools. They sample prompts, AI answers vary between runs and users, and results depend on the prompts you choose. Use them for direction and competitor comparison, not as exact counts.
A simple monthly scorecard
| Metric | Source | Cadence |
|---|---|---|
| Clicks and impressions for target pages; AI impressions | Search Console (Performance and Generative AI reports) | Monthly |
| Referral visits from AI products | GA4 custom channel group | Monthly |
| Citations and grounding queries | Bing Webmaster Tools | Monthly |
| Bot fetches | Server logs | Quarterly |
| Brand mentions in sampled prompts | AI visibility tool | Monthly |
| Conversions from the above visits | GA4 | Monthly |
Always tie the numbers to outcomes: sign-ups, demos or sales. Citations that bring no qualified visits are a vanity metric.
What doesn’t work, and common myths
- “You need an llms.txt file.” It is a proposal. Google says no special files are needed for its AI features, and the other vendors’ crawler pages used here don’t mention it.
- “You need special AI schema.” Google says no special schema.org markup is needed for AI Overviews or AI Mode.
- “Blocking GPTBot hides you from ChatGPT search.” OpenAI separates the two: OAI-SearchBot controls search appearance, GPTBot is for training.
- “Allowing a bot in robots.txt is enough.” Not if your firewall blocks it. OpenAI asks you to allow its published IP ranges too.
- “GEO tactics guarantee a lift.” The GEO paper reports gains of up to 40% on its own benchmark, with results that vary by domain. That is research, not a service-level promise.
- “Robots.txt keeps pages private.” Google says it doesn’t keep a page out of Google. Use
noindexor authentication. - “Keyword stuffing for AI.” No official source recommends it, and Google’s guidance points to helpful, original content.
- “Mass-produce AI articles to dominate answers.” Nothing in the documentation supports it. Original information is what gives a source a reason to be cited. If you use AI for drafts, edit and add your own data and expertise.
- “Fake structured data to look more authoritative.” Google’s policy says markup must reflect visible content, and violations can lead to manual actions.
A 30-day plan
Week 1: access.
- Audit robots.txt, WAF and CDN rules for OAI-SearchBot, PerplexityBot and Bing.
- Verify the site in Search Console and Bing Webmaster Tools.
- Submit sitemaps and consider IndexNow.
Week 2: measurement.
- Create a GA4 custom channel group for AI referrals.
- Record a baseline: clicks, referrals, citations.
- Pick 10–20 target questions and note which brands AI tools cite for each. Do this by hand first.
Week 3: content.
- Rewrite your top 5 pages so each opens with a direct answer.
- Add the follow-up questions a buyer would ask.
- Update stale facts and dates.
Week 4: reputation and review.
- Fix inconsistent brand descriptions across your profiles.
- Plan one original data asset you can publish: a survey, benchmark or case study.
- Review the scorecard and decide what to repeat.
Choose where to start
- Small site with limited time: check crawler access and fix your answer-first structure on your best pages. These are cheap and documented.
- Content team at a growing company: add a measurement setup (GA4 channel group, Bing Webmaster Tools) and build original-data content.
- Agency or enterprise: add an AI visibility tool for prompt tracking across clients, and keep manual spot checks for accuracy.
AI search changes quickly, and vendors update their documentation without notice. Re-check the official pages linked in the sources before you make decisions based on this guide.