GEO, AEO, LLMO, AIO: AI Search Acronyms Decoded

Search for an SEO for AI acronym and you'll find five or six candidates, none of them official. Here is what each term means and what kind of term it is.

GEO has the clearest origin. It comes from a 2023 research paper by Aggarwal and colleagues that tested content edits in a lab engine. AEO and LLMO grew out of marketing. For a fuller comparison of the first three labels, see AEO vs GEO vs AI SEO.

A useful test: does a platform document the term in its own help pages? If yes, it names something real. If only vendors and agencies use it, it's a label for a service.

Real, documented by the platforms

• AI Overviews and AI Mode. Google documents both for site owners. Its AI features page says a page must be "indexed and eligible to be shown in Google Search with a snippet" to appear as a supporting link, and that there are "no additional technical requirements."
• Grounding. Google's Gemini API offers "Grounding with Google Search" as a named feature. Microsoft sells "Grounding with Bing Search" on Azure. It is a product term, not a tactic.
• RAG. A standard term from AI research for the retrieve-then-generate pattern most AI search tools use.

Labels for a service

• GEO, AEO, LLMO, AIO. No engine documents a GEO score, an AEO system or an LLMO ranking. Google's optimization guide says you "don't need to write in a specific way just for generative AI search."
• AI SEO. Useful in conversation, but it carries two different meanings, so ask which one someone means.

One extra trap: GEO also means geographic or local SEO in some agencies. Read GEO SEO: generative engines or geographic search before you sign a brief with that word in it.

The acronyms hide the mechanism. Every AI answer passes through up to four stages, and each one has a different owner.

1. Parametric knowledge. What the model learned in training, frozen at a cutoff date. Nobody changes this in the short term.
2. Access. Whether a crawler is allowed to fetch and store your page. You and your CDN control this.
3. Retrieval. Whether your page is pulled into the model's context for a given question. The engine decides this, per query.
4. Generation. Which retrieved pages the model uses and cites. The model decides this.

RAG and grounding describe stages 2 to 4 working together. When an engine grounds an answer, it runs searches, reads results and writes from them.

Two details matter. First, retrieval is optional and discretionary. As of October 2026, ChatGPT decides per question whether to search, and many answers come from training data alone. Second, the engine writes its own search queries. OpenAI's help documentation says ChatGPT search rewrites a prompt into one or more targeted queries. So the question a user types is not the query that finds your page.

This is why one acronym can't capture the work. Some of it is technical access. Some of it is being worth retrieving. Some of it is out of your hands.

The honest split is narrower than most GEO pitches suggest.

In your control

• Per-bot crawler access. robots.txt rules apply bot by bot. OpenAI says its crawler settings are independent: GPTBot covers training, and OAI-SearchBot covers ChatGPT search.
• Rendering. Most AI crawlers other than Googlebot don't run JavaScript. Content that only appears after scripts run may be invisible to them.
• Reachability. A firewall or CDN can block a crawler that robots.txt allows.
• Snippet controls. noindex, nosnippet, max-snippet:0 and data-nosnippet keep content out of AI answers, sometimes by accident.
• Clear facts. Stating a price, a number or a one-line definition on a page that can be fetched gives an engine a sentence it can quote.

Not in your control

• What the model learned in training.
• Which pages an engine retrieves for a given question.
• Whether you get cited, and where in the answer.

The research backs this split. A 2026 critical survey of 45 GEO studies concluded that "no reviewed technique shows a stable, longitudinal, cross-platform causal effect on organic discoverability." The original GEO paper's 30 to 40% gains were measured in a lab engine on content already placed in the model's context. That is a stage 4 result, not proof that a page gets found. For the full picture, read what you can and can't influence with LLM optimization.

Whatever your team calls it, the work runs in the same order. Access comes first because nothing downstream matters if a crawler can't read the page.

1. Check robots.txt per bot. Make sure retrieval bots such as OAI-SearchBot, PerplexityBot and Claude-SearchBot are allowed. Block training bots separately if you want to. For example:

User-agent: OAI-SearchBot
Allow: /

User-agent: GPTBot
Disallow: /

2. Test what crawlers actually receive. robots.txt can say yes while your firewall says no. The AI Crawler Checker requests a page as each AI crawler and shows which ones get turned away.
3. Put key facts in the HTML. Pricing, product names and definitions should be in the page before JavaScript runs.
4. Remove accidental suppression. Look for template-level nosnippet or data-nosnippet that hides more than intended.
5. Confirm index status per engine. Check Google Search Console and Bing Webmaster Tools separately. Copilot grounds on Bing, so one healthy index tells you nothing about the other.
6. Fix contradictions on your own site. If your pricing page and footer disagree, an engine may repeat either one.
7. See what AI says now. The free checker on this site shows whether AI engines name you for up to three buyer questions. Treat it as a snapshot.

If you work with an agency, ask whether this list is already in scope. The question of whether your SEO agency should handle AI search usually comes down to steps 1 to 5.

New labels tend to carry old advice. These are the claims to push back on.

• "Add schema to get cited." Google says structured data isn't required for generative AI search. Ahrefs' matched test of 1,885 pages found AI Overview citations fell 4.6% after schema was added. Existing markup does no harm. It just isn't a lever for AI answers.
• "Publish an llms.txt." Google says Search ignores it, and no other engine documents reading it.
• "Allow GPTBot so ChatGPT can find you." GPTBot governs training. ChatGPT search uses OAI-SearchBot.
• "Improve your E-E-A-T score." Google says E-E-A-T isn't a specific ranking factor. There is no score.
• "Track your AI rank." Google's AI reporting exposes impressions only, with no position data to check a rank against.
• "Write in an AI-friendly style" or "chunk your content." Google rejects both and says there is no ideal page length.
• One blended score. Engines retrieve differently and cite differently. A single number averages answers given with and without live search. Look at each engine separately.

The pattern is the same each time: an acronym gets attached to a tactic, and the tactic gets sold as a feature of the engine.

Pick one term and define it by outcome. "Being named accurately in AI answers for the questions our buyers ask" is clearer than any acronym.

If you need a name for a job title or a budget line, GEO and AEO are the most widely understood. Short definitions live in the glossary for answer engine optimization and LLM optimization. For how the names compare in search and in hiring, see what SEO for AI is called.

Then hold vendors to the work, not the word. Ask three questions:

• Which crawler access checks do you run, and do you test the firewall as well as robots.txt?
• Do you report each AI engine separately?
• Which of your numbers are measurements, and which are your own model?

A provider with clear answers is doing the real work, whatever it calls it.

Is GEO the same thing as AEO? In practice, yes. Both describe getting AI tools to mention and cite your brand. GEO comes from a 2023 research paper and leans toward generative engines like ChatGPT and Gemini. AEO grew out of marketing and leans toward answer engines and featured answers. No platform treats them differently, because no platform recognizes either term. Choose whichever your team and buyers already understand.

Did Google create GEO or AEO? No. Google uses product names such as AI Overviews and AI Mode, and its guidance says those features have no additional requirements beyond normal Search eligibility. Its optimization guide also says you don't need to write in a special way for generative AI search. GEO, AEO and LLMO come from researchers, vendors and agencies, not from any engine's documentation.

What does AIO mean? It depends on who's talking. Some vendors use AIO for AI optimization, a generic label for the whole practice. Many SEO practitioners use it as shorthand for Google AI Overviews, the AI summary above some results. If a report or proposal uses AIO, ask which one it means. A drop in AI Overviews is a specific Google event, while AI optimization covers every engine.

Can I optimize for RAG or grounding directly? Not directly. RAG and grounding are how the engine works, and the engine writes its own search queries and picks what to read. What you can do is make sure the retrieval step can reach you: allow the right crawlers, serve key content without JavaScript, keep servers reachable to bots and stay indexed. After that, retrieval and citation are the engine's decision.

Do AI Overviews and AI Mode work the same way? Not exactly. Google says AI Overviews and AI Mode may use different models and techniques, so they can show different links for the same question. Both require a page to be indexed and eligible for a snippet in Google Search. As of October 2026, Google offers no robots.txt token that blocks either one specifically. Googlebot rules and snippet controls are the levers.