If you have spent time in the AI search space in 2026, you have seen three terms used interchangeably, sometimes contradictorily, and occasionally as marketing vehicles: SEO, AEO, and GEO. Practitioners cannot agree on which one is correct. Vendors use whichever one fits their positioning. Job listings mix them in the same bullet point.
The terminology confusion is real but the underlying problem is not complicated. All three terms describe some aspect of the same challenge: how do you remain visible as AI systems increasingly mediate what information buyers receive?
This post maps each term precisely, shows where they overlap and where they differ, and gives you a clear framework for thinking about your own programme.
What Is SEO in 2026?
Search Engine Optimisation is the practice of making your website discoverable, crawlable, and authoritative so that search engines rank your pages for relevant queries. In 2026, SEO is still the technical and content foundation everything else builds on.
If your pages cannot be crawled, nothing in AI search works either — Google's standard index feeds both AI Overviews and AI Mode. If your content has no topical authority, AI engines have less reason to trust it. If your entity signals are weak, AI systems cite you with less confidence. Every piece of good SEO work improves AEO and GEO performance because they all start from the same infrastructure.
What SEO does not address is the answer layer. Ranking position one does not guarantee a brand appears inside an AI Overview or AI Mode response. Ahrefs confirmed the overlap between AI Overview citations and Google's top-ten results dropped from 76% to 38% between mid-2025 and early 2026. The channels are diverging. SEO alone no longer covers the full search visibility problem.
What Is AEO (Answer Engine Optimisation)?
Answer Engine Optimisation is the discipline of structuring content so AI systems select it as the source when generating answers. AEO emerged from featured snippets and voice search — early forms of "answer layer" in search — and expanded dramatically as ChatGPT, Perplexity, and Google AI Mode grew into primary research tools.
AEO focuses on the retrieval and citation layer: ensuring your content passes the filters an AI engine runs before including a source in its answer. That means passage-level extractability (direct answers in the first 40 to 60 words of each section), question-format headings, FAQPage schema, fresh dateModified signals, and verifiable claims with named sources.
AEO is where most practitioners land when they ask "how do I get cited in AI answers?" It is the most action-oriented of the three terms because it maps directly to specific content and technical changes you can make today.
What Is GEO (Generative Engine Optimisation)?
Generative Engine Optimisation is the broader discipline that AEO sits inside. While AEO targets the citation layer — the retrieval and source-selection step — GEO also encompasses entity optimisation, brand authority building inside AI model training data, and influencing how AI systems represent your brand even in responses that do not cite you directly.
The GEO concept originated in academic research. A Princeton, Georgia Tech, and IIT Delhi paper from 2023 formally defined the term and documented which content strategies improve citation rates in AI-generated responses. It showed that citing authoritative sources in your own content, adding statistics, and using a fluent, quotable writing style each independently increased the probability of citation in generative AI responses.
In practice, GEO adds two dimensions that pure AEO work often misses. First, brand authority in AI model training: the degree to which your brand is accurately represented in the model's parametric memory, not just its live retrieval. Second, off-site entity building: establishing consistent brand signals across Wikidata, Wikipedia, Reddit, review platforms, and industry publications — the external corroboration that increases how confidently AI models cite you. The entity-based AEO guide covers the technical side of this in full.
What Is LLMO and How Does It Fit?
LLMO — Large Language Model Optimisation — is a fourth term you will encounter. It refers specifically to optimising for the parametric knowledge layer of AI models: the information encoded in their weights during training rather than retrieved from the web at query time.
LLMO matters for brand reputation and accuracy. When ChatGPT describes your product without running a web search, it is drawing from parametric memory. If that memory contains outdated or inaccurate information — an old product name, a discontinued pricing tier, a feature you removed — LLMO addresses how to correct it. The brand hallucination guide covers the diagnostic and correction process.
For most content teams, LLMO is a background concern addressed through consistent brand entity signals across high-authority third-party sources. The same work that improves GEO entity authority also improves the accuracy of model parametric memory over training cycles.
The One-Paragraph Summary of How They Relate
SEO is the technical and content foundation. AEO is the citation-layer discipline that sits on top of that foundation — getting your content retrieved and selected by AI engines for specific queries. GEO is the broader brand authority discipline that includes AEO plus entity building, off-site corroboration, and model memory accuracy. LLMO is a subset of GEO specifically addressing parametric model knowledge. Every term describes a real problem. The tactics they point to are largely the same. The framing changes depending on which layer of the AI search system you are focusing on.
Does the Term You Use Actually Matter?
For practitioners, no. Use whichever term your audience understands. AEO is the most widely recognised in SEO circles. GEO appears more often in academic and enterprise contexts. LLMO is used most by AI researchers.
For building a programme, the useful distinction is between three layers that need separate attention:
- Retrieval layer: Can AI crawlers reach your pages? Does your content pass the technical filters at the source-selection stage? This is AEO's core territory.
- Citation layer: When your page is in the candidate set, does your content earn a citation? Passage structure, schema, freshness, and answer-first formatting determine this.
- Entity layer: How confidently do AI systems represent your brand? Off-site corroboration, consistent entity signals, and Wikidata/Wikipedia presence determine this. This is GEO's additional territory beyond AEO.
Most teams underinvest in the entity layer because it does not map to familiar content tasks. It requires building profiles, earning editorial mentions, maintaining Wikidata entries, and engaging in community platforms — work that feels more like PR than SEO. But 85% of AI brand mentions originate from third-party sources. The entity layer is where most of the citation advantage lives. The off-site AEO signals guide is the starting point for this work.
NotionCue tracks your brand's citation performance across all five major AI engines. It monitors citation rate per prompt, AI share of voice against competitors, and brand description accuracy — covering both the retrieval layer (AEO) and the entity layer (GEO) in one dashboard. Start with the AI Crawler Audit to confirm the technical foundation is solid, then use the Prompt Tracker to establish your baseline across engines.
Which Should You Focus on First?
Fix in this order. SEO foundation first — crawl access, indexation, site speed, basic content quality. AEO retrieval layer second — schema, passage structure, answer-first headings, dateModified freshness. GEO entity layer third — review platforms, Wikidata, Reddit, earned editorial coverage. LLMO last — model memory improves naturally as your entity signals strengthen across high-authority sources.
Teams that skip to GEO entity building without fixing their technical crawl access produce off-site signals that point to pages AI crawlers cannot reach. Fix the access first. The entity work compounds on top of a working technical foundation, not instead of one.
Frequently Asked Questions
Is GEO replacing SEO?
No. GEO adds a layer to SEO. The technical foundations — crawlability, indexability, site speed, content quality — are prerequisites for both SEO rankings and GEO citations. A brand that abandons SEO to focus only on GEO loses the organic ranking signals that feed AI retrieval candidate pools in the first place.
Which AI engines use parametric memory versus live retrieval?
ChatGPT and Claude primarily use parametric memory (model training data) but activate live retrieval when the query needs current information or when the user enables search. Perplexity always uses live retrieval. Google AI Overviews and AI Mode use Google's standard index. This is why the same brand can appear in Perplexity within days of a content change but take months to update in ChatGPT's default responses.
Do I need separate tools for AEO and GEO tracking?
Not necessarily. The core measurement is the same: citation rate per prompt per engine. Whether you call it AEO tracking or GEO tracking, the data you need is how often your brand appears in AI answers for your target queries. The NotionCue Prompt Tracker covers this across all five major engines in one place.
Is LLMO something most brands need to actively work on?
Only when AI systems consistently describe your brand inaccurately — wrong product names, outdated pricing, features you removed. In that case, the fix is through off-site entity signals, not any direct intervention in AI model training. Consistent, accurate entity information across Wikidata, G2, LinkedIn, and Crunchbase propagates into model training data over time.