Answer Engine Optimization (AEO) vs GEO vs AI SEO: What Each Means and What to Do

In brief: Answer engine optimization (AEO) is the work of making your content the answer an AI system or search feature gives. GEO (generative engine optimization) is the same idea named after the generative engines doing the answering. LLM SEO, AI SEO, AIO and LLMO are looser labels for the same territory. They all sit on top of classic SEO, not beside it. In practice the work is five things: answer questions plainly, make your entity and facts unambiguous, earn mentions on sources the engines trust, keep pages fresh, and let the right crawlers in. Measure it with citation tracking, AI referral traffic and brand mentions, not rankings alone.

Reviewed on 29 October 2026 by Gaurav Phogat. Claims about how each engine works are checked against the platforms’ own documentation on the same date.

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Every few months a new acronym arrives in my inbox from an agency pitch. AEO, GEO, LLMO, AIO, AI SEO. Marketing leaders ask me which one they should budget for. My answer is that they are mostly one discipline wearing different badges, and the label matters far less than the order in which you do the work. This guide gives plain definitions, a comparison table, what the platforms themselves say about how they choose sources, and a 60-day plan. I use healthcare as the worked example throughout because it is the hardest case: high trust bar, strict advertising rules and patients who now ask chat assistants before they ask Google.

What is answer engine optimization?

Answer engine optimization is the practice of structuring content, facts and reputation so that systems which return answers rather than lists of links pick your content as the answer, or cite you as a source for it. Before generative AI, the same idea applied to featured snippets, voice assistants and “People also ask” boxes, where Google returned one answer instead of ten blue links. Generative AI widened the surface: Google AI Overviews and AI Mode, ChatGPT search, Perplexity, Gemini and Microsoft Copilot all compose an answer and attach sources.

For a hospital, the AEO question is simple. When someone asks “what is the recovery time after knee replacement” or “which hospitals in Pune do knee replacement”, is your page the one the answer is built from, and is your name in it?

GEO, LLM SEO and AI SEO in one line each

• GEO (generative engine optimization): the term from a 2023 research paper by researchers at Princeton University and IIT Delhi, among others, for improving how visible content is inside answers written by generative engines. I cover the healthcare version in depth in my guide to how hospitals get cited by AI search, and the glossary has a short definition of GEO.
• LLM SEO (or LLMO): optimising for large language model products specifically, such as ChatGPT, Gemini, Claude and Perplexity, including how they describe your brand when they do not search the web at all.
• AI SEO: the loosest label. Agencies use it for two different things: optimising for AI search, and using AI tools to do SEO work faster. Always ask which one is being sold.
• AIO: sometimes “AI optimisation”, sometimes shorthand for Google’s AI Overviews. Check the context.

AEO vs GEO vs LLM SEO vs AI SEO vs classic SEO: the comparison table

The honest version of this table shows a lot of overlap. That is the point: one well-run programme covers all five columns.

If you want the hospital-specific version of the SEO and GEO split, including how I split budget between them, read SEO vs GEO for hospitals.

How answer engines pick their sources: what the platforms actually say

A lot of AEO advice is guesswork presented as fact. Here is only what each platform states in its own documentation, as of the date above.

Google AI Overviews and AI Mode

Google’s documentation on AI features says a page must be indexed and eligible to show in Search with a snippet, and that there are no additional technical requirements. It states that you do not need new machine-readable files, AI text files or special schema to appear. Both features may use “query fan-out”, running several related searches across subtopics, which is why a page can be cited for a question it never ranked for. Clicks from these features are counted inside Search Console’s normal Performance report under the Web search type, not separately. Your controls are the usual ones: nosnippet, max-snippet, noindex and robots.txt. I wrote about one specific test, Google pointing AI answers to the official page for a patient task, in Google AI Mode and official hospital pages.

Gemini

Gemini apps ground answers in content from the Google Search index. Google’s crawler documentation says the Google-Extended token controls whether your content is used for Gemini training and for grounding in Gemini apps, and that it does not affect inclusion or ranking in Google Search. So blocking Google-Extended is a Gemini decision, not an SEO one.

ChatGPT search

OpenAI’s help article on ChatGPT search says answers that use search carry inline citations, and that queries may be shared with third-party search providers such as Bing. Its crawler documentation separates three agents: OAI-SearchBot (search; sites that block it will not be shown in ChatGPT search answers), GPTBot (model training) and ChatGPT-User (fetches a page when a user asks). The settings are independent. Blocking GPTBot to opt out of training does not remove you from ChatGPT search; blocking OAI-SearchBot does.

Perplexity

Perplexity’s bot documentation says PerplexityBot is what surfaces and links sites in its results, and recommends allowing it in robots.txt. Perplexity-User fetches pages when a user asks a question and generally ignores robots.txt because the user requested it.

Microsoft Copilot and Bing

In February 2026 Bing Webmaster Tools launched an AI Performance report in public preview. It shows how often your pages are cited across Copilot, AI summaries in Bing and some partner integrations, plus the “grounding queries” the AI used to retrieve your content. Bing’s own guidance in that post is unglamorous: clear headings, tables and FAQ sections, evidence and cited sources, accurate and up to date content (with IndexNow to notify changes), and current Bing Places listings for local businesses.

For the next step, see AI in Healthcare Marketing, GA4 setup for hospital websites and Package pricing vs itemised billing.

Read across all five and a pattern appears. None of them asks for a special file or a secret markup. All of them need to be allowed to crawl you, all of them reward pages that answer clearly and carry evidence, and most of them draw on a conventional search index underneath. That is why I tell teams that answer engine optimization starts with SEO hygiene.

What my AI hospital recommendations study showed

In October 2026 I asked ChatGPT, Gemini and Perplexity one fixed question, “best hospital for [specialty] in [city]”, across six specialties and up to nine Indian cities, and logged every answer. The full method and dataset are in the AI hospital recommendations study. Four findings change how I think about AEO:

• Answers are shortlists, not verdicts. All 91 answers named three to eight hospitals. The aim is to be on the shortlist, which is a corroboration problem more than a ranking problem.
• Your own website is the main evidence. Of 376 linked sources, 86% were hospital-owned websites. Service and doctor pages that state facts plainly do the heavy lifting.
• Engines disagree. For the same Delhi and Mumbai prompts, ChatGPT named a government hospital in 75% of answers and Gemini in 33%. Gemini leaned more on third parties: 36% of its source labels were insurers, crowdfunding sites, medical tourism marketplaces or blogs. One engine is not a proxy for the others.
• Being read is not being recommended. 42 of the 88 hospital-owned pages Perplexity linked belonged to hospitals that did not make its shortlist. Your page can inform an answer that names a competitor.

That last point is the one I see marketing leaders miss. Traffic and even citations are not the outcome. Being named, accurately, is.

What to do: the six workstreams of answer engine optimization

1. Content structure: answer first, then explain

Put a two to four sentence answer directly under each question heading, then the detail. Use question-shaped H2s and H3s that mirror how people ask. Use tables for comparisons, numbered steps for processes, and a short FAQ for the follow-ups. On a hospital procedure page that means the first screen answers who it is for, how long the stay usually is, what recovery looks like and which doctors perform it, all clinically reviewed, before any brand copy. Generic “centre of excellence” paragraphs give an engine nothing to quote.

2. Entity and schema: make the facts unambiguous

An engine has to be sure who you are before it will name you. Keep one canonical name, address and phone per location everywhere: website, Google Business Profile, Bing Places, directories. Give every doctor one profile page with qualifications and registration details, and link doctors to the services and locations they belong to. Schema does not get you into AI answers on its own (Google says no special schema is needed), but accurate Organization, Hospital or MedicalClinic, Physician and FAQ markup removes ambiguity. My structured data guide for hospitals has the field-by-field version, and SEO for doctors covers the doctor profile side.

3. Citations and digital PR: build the corroboration layer

Engines prefer claims they can see confirmed in more than one place. That means credible third-party mentions: health journalists quoting your clinicians, listings on insurer and government directories, association pages, and original data others cite. The Gemini pattern in my study is a reminder that insurer and aggregator pages are part of the answer whether you like it or not, so the facts about you on those pages need to be right. Original research is the most durable asset here; it gets cited by people, and then by machines reading those people.

4. Freshness: date it and mean it

Show a visible “last reviewed” date and a named reviewer on medical content, and actually review it. Update doctor rosters, timings and package details when they change, not once a year. Bing explicitly ties accuracy and freshness to citation and recommends IndexNow, which most WordPress SEO plugins and hosts can enable in minutes.

5. Technical access: robots.txt, rendering and llms.txt

Check your robots.txt against each agent separately. I regularly find sites that blocked “all AI bots” in a hurry and removed themselves from ChatGPT search and Perplexity without meaning to. A sensible default for most brands is to allow the search agents (Googlebot, Bingbot, OAI-SearchBot, PerplexityBot) and make a separate, deliberate decision on the training agents (GPTBot, Google-Extended). Make sure key facts are in the HTML, not only inside JavaScript widgets, PDFs or images.

On llms.txt, the plain position: it is a proposal published by Jeremy Howard in September 2024, not an adopted standard. Google’s documentation says you do not need AI text files to appear in its AI features, and Google’s John Mueller has said none of the AI services have said they use it, as reported by PPC Land. It costs little if your SEO plugin generates one, but do not let it displace real work.

6. Measurement: what you can and cannot see

• AI visibility tracking: fix a question set (I use 30 to 60 questions per service line, written the way patients ask), run it on a schedule across engines, and log whether you are named, cited, and described accurately. My free AI answer tracker is built for this, and the AI search visibility audit scores the on-site basics.
• Platform data: Search Console folds AI Overview and AI Mode clicks into Web search totals, so watch question-shaped queries and landing pages. Bing’s AI Performance report gives citation counts and grounding queries for Copilot.
• Referral traffic: in analytics, create a channel group for referrals from chatgpt.com, perplexity.ai, gemini.google.com and copilot.microsoft.com. Some clicks arrive without a referrer and land in Direct, so treat this as a floor.
• Brand mentions: track unlinked mentions and branded search volume. When AI answers name you, branded searches tend to follow.

A 60-day answer engine optimization plan

This is the sequence I would give a marketing team starting from zero, using one service line as the pilot. In a hospital, pick a service line where you have strong doctors and clean facts, such as orthopaedics or cardiology.

Sixty days is enough to see entity and page changes reflected in some engines and not in others. Expect noise. Answers change day to day, so compare patterns across the whole question set, not single answers.

Myths about AEO and GEO

• “SEO is dead, AEO replaces it.” Google’s own eligibility rule for AI features is being indexed and snippet-eligible, and ChatGPT search may use third-party search providers such as Bing. Weak SEO means weak AEO.
• “You need llms.txt or special schema to be cited.” Google says neither is required, and llms.txt is still a proposal.
• “Blocking AI bots protects us.” Blocking search agents removes you from answers. Decide on training and search access separately.
• “We can buy our way into AI answers.” You can buy ads around them. Citations come from content, reputation and corroboration.
• “One engine is enough to track.” My study showed the same question producing different shortlists and different source mixes across engines.
• “Citations equal recommendations.” Nearly half the hospital pages Perplexity linked in my study (42 of 88) were from hospitals it did not shortlist.
• “There is a guaranteed tactic.” The original GEO paper reported visibility gains of up to 40% in its tests, and also that what works varies by domain. Treat any vendor promise of guaranteed citations as a red flag.

What changes for healthcare and other regulated industries

Google classes health as a “Your Money or Your Life” topic. Its guidance on helpful content says its systems give extra weight to experience, expertise, authoritativeness and trust on topics that could significantly affect health, financial stability or safety, and that trust matters most. For answer engines, that raises the bar in three ways. I explain the Google side in detail in YMYL and E-E-A-T for medical websites.

• Named, qualified authorship. Every clinical page needs a named reviewer with credentials and a review date. Anonymous health content is easy for an engine to skip.
• Compliance shapes the answer you can give. In India the NMC’s 2026 advertising guidelines rule out testimonials, superlatives about hospitals and cure claims in doctors’ communication; my NMC 2026 guidelines hub has the detail. AEO copy has to answer clearly within those limits. “Leading”, “best” and “guaranteed results” are both non-compliant and unhelpful to an engine looking for facts.
• Accuracy beats volume. An AI answer that repeats a wrong fact from your outdated page is a patient safety and reputation problem, not just a marketing one. Retire or fix stale content before writing new.

The same logic holds for financial services, insurance and legal brands: the stricter the rules, the more the work shifts from clever copy to verified facts, credentials and corroboration.

How I would brief an agency on AEO

Ask for three things in writing. First, the question set they will track and on which engines, with a baseline before any work starts. Second, which of the six workstreams above they own and which stay in-house (entity facts and clinical review usually must stay in-house). Third, how they will report being named versus being cited versus referral traffic. If a proposal leads with a proprietary “AI ranking score” and cannot explain the questions behind it, keep looking.

Questions people ask

Sources

• Google Search Central: AI features and your website
• Google: common crawlers, including Google-Extended
• Google: creating helpful, reliable, people-first content
• OpenAI: ChatGPT search
• OpenAI: overview of OpenAI crawlers
• Perplexity: Perplexity crawlers
• Bing Webmaster Blog: AI Performance in Bing Webmaster Tools (February 2026)
• llmstxt.org: the llms.txt proposal
• PPC Land: llms.txt adoption stalls
• Aggarwal et al., GEO: Generative Engine Optimization (arXiv)