AEO (Answer Engine Optimization) targets Google's AI Overviews and featured snippets. GEO (Generative Engine Optimization) targets being cited by AI chat tools like ChatGPT, Perplexity, and Gemini. LLMO (Large Language Model Optimization) is a broader, related term covering how content is structured to be understood and trusted by large language models generally — it overlaps heavily with GEO and the two terms are often used interchangeably. All three sit on top of solid traditional SEO; none of them replace it.
Key Takeaways
- AEO, GEO, and LLMO are not competing strategies — they're different names for closely related outcomes of the same underlying content quality work.
- AEO is Google-specific (AI Overviews, featured snippets); GEO and LLMO are about third-party AI tools that pull from the open web.
- All three reward the same core things: clear direct answers, structured data, verifiable authorship, and content that reads well for humans first.
- There is no separate 'AI content strategy' worth running alongside your regular content strategy — the same well-built page should serve all of these simultaneously.
- Measuring AEO/GEO success requires manually checking AI Overviews and prompting AI tools directly — there isn't yet a mature analytics dashboard equivalent to Search Console for this.
These three acronyms get thrown around interchangeably, which causes real confusion. Here's what each one actually means, where they overlap, and — more usefully — what to actually do about them. For the fuller picture of how all this fits into a working SEO strategy, see SEO in 2026: the complete AI search strategy guide.
| Term | Targets | Primary channel | Earned by |
|---|---|---|---|
| AEO | Google AI Overviews, featured snippets | Google only | Clear, self-contained answer near the top of the page |
| GEO | Citations in AI chat answers | ChatGPT, Perplexity, Gemini, Claude | Verifiable facts, structured data, citable original content |
| LLMO | Accurate representation by language models | Training data + live retrieval | Consistent, accurate public information a model can trust |
AEO — Answer Engine Optimization
AEO specifically targets Google's own AI-generated answers: AI Overviews and, before that, featured snippets. When someone searches "what is technical SEO," Google may generate a synthesized answer at the top of the results page, sourced from a small number of pages it trusts enough to quote — see Google's own documentation on AI features.
What earns AEO visibility: a clear, complete, self-contained answer appearing early on the page — ideally in the first paragraph after the heading that matches the question. Structured formatting (numbered steps, definition-style opening sentences) also helps Google's extraction process identify a quotable answer.
GEO — Generative Engine Optimization
GEO targets visibility inside AI chat tools — ChatGPT, Perplexity, Gemini, Claude — when they generate answers to user questions, sometimes citing sources and sometimes not. Unlike AEO, this isn't just about Google; it's about how the entire open web (and each tool's own retrieval/training process) perceives your site's authority and clarity.
What earns GEO visibility: consistent, verifiable facts about your business across the web (matching NAP data, consistent claims), clear structured data, original information that's genuinely worth citing (data, expert opinions, specific numbers), and a site that's easy for AI crawlers to access and parse.
LLMO — Large Language Model Optimization
LLMO is the broadest of the three terms, referring generally to optimising content so large language models understand, trust, and can accurately represent it — whether that's during a model's training process or in real-time retrieval. In practice, LLMO overlaps so heavily with GEO that most practitioners use the terms interchangeably; some reserve LLMO specifically for training-data-level optimisation versus GEO's focus on live retrieval and citation.
None of these three replace traditional SEO or the E-E-A-T and structured-data fundamentals — they're additional outcomes of the same underlying work, covered in full in our SEO in 2026 guide and, for the practitioner skill-building side of this, in 6 AI SEO skills every marketer needs.
Which one should you prioritise first?
If you can only focus on one this quarter, start here:
- Small local business, most traffic from Google search → start with AEO
- B2B or SaaS where buyers research via ChatGPT/Perplexity → start with GEO
- Content-heavy publisher wanting long-term AI trust → invest in LLMO alongside GEO
How to actually measure this
Unlike traditional SEO, there's no mature analytics dashboard yet for AEO/GEO performance. The practical approach: periodically search your target queries in Google to check for AI Overview appearances, and directly prompt ChatGPT, Perplexity, and Gemini with questions relevant to your business to see if and how you're mentioned. It's manual, but it's currently the most reliable way to track this.
Conclusion
AEO, GEO, and LLMO are useful shorthand for a real shift in where search traffic comes from, but they're not a reason to abandon SEO fundamentals or run a separate content strategy. Build pages that answer questions clearly, back them with structured data, and make your expertise verifiable — that single approach is what earns visibility across traditional search, AI Overviews, and AI chat tools at the same time.
Source: Google Search Central — AI features.
Frequently Asked Questions
They overlap heavily and are often used interchangeably. Where a distinction is drawn, GEO usually refers to optimising for live citation in AI tools' generated answers, while LLMO more broadly covers how content is structured to be understood and accurately represented by language models in general, including during training.
No — and be cautious of anyone selling AEO/GEO as an entirely separate service from SEO. The work overlaps so heavily that it should come from the same team doing your core SEO, not a disconnected add-on.
Yes, more easily than in traditional SEO in some cases — AI Overviews often favour the page with the clearest, most complete direct answer regardless of domain size, since the goal is quality of the extracted answer, not just domain authority.
Directly ask the tools questions relevant to your business and industry, and check whether your site appears as a source. Some tools show citations explicitly; for others, you'll need to check if the factual claims in the answer match information unique to your site.
About the Author
Satish Prajapati
Google Ads, Meta Ads & Social Media, Digital Aura
Satish Prajapati
Google Ads, Meta Ads & Social Media, Digital Aura
Satish runs paid advertising at Digital Aura — Google Ads and Meta Ads campaigns for clients who need results they can measure, not just impressions. He handles everything from campaign structure and audience targeting to ad creative and budget allocation, adjusting spend toward whatever's actually converting. Most of his campaigns run across both platforms at once, so a client isn't relying on a single channel for their paid traffic.
He treats a campaign's first few weeks as a testing phase, not a finished product — running multiple ad variations and audience segments to see what actually performs before scaling budget behind it. He checks cost-per-result and return on ad spend closely, and cuts what isn't working instead of leaving underperforming ads running out of habit. That keeps client budgets going toward what's proven, not what looks good on paper. He also keeps a close eye on organic social performance, since a Reel or post that's already working organically is often the first thing worth turning into a paid campaign.
Reviewed by: Sambhav Shah