A 2026 search marketing survey by Search Engine Journal found that 62% of marketers could not accurately define the difference between GEO, AEO, and LLMO — and 34% thought they were three names for the same thing. The terminology confusion in AI search optimisation is not just academic. It leads to misaligned strategies, wasted budgets, and businesses buying services they do not need while missing the ones they do.
This article cuts through the noise. We will define each term precisely, show where they overlap and diverge, and help you understand which approach — or combination — your business actually needs.
The Definitions
GEO — Generative Engine Optimisation
GEO is the practice of optimising your brand’s visibility within AI-generated answers. The term was coined by researchers at Princeton, Georgia Tech, IIT Delhi, and the Allen Institute in their landmark 2023 paper. It specifically addresses the challenge of earning citations and mentions inside the synthesised responses produced by generative AI systems like ChatGPT, Google AI Overviews, Perplexity, and Gemini.
GEO is the broadest of the three terms. It encompasses all the signals, strategies, and technical implementations that influence whether an AI model cites your content when generating a response.
AEO — Answer Engine Optimisation
AEO predates GEO and originally referred to optimising content for featured snippets and direct answer boxes in traditional search engines. As AI search evolved, AEO’s scope expanded to include optimisation for AI-powered answer systems — any platform that provides direct answers to user queries rather than a list of links.
AEO tends to focus more narrowly on content structure and format — ensuring your content is the one that gets selected as the direct answer to a specific question.
LLMO — Large Language Model Optimisation
LLMO is the newest of the three terms and the most technically specific. It refers to optimising your brand’s representation within the knowledge and training data of large language models themselves — not just the search interfaces built on top of them.
LLMO addresses the underlying model rather than the output interface. It asks: when GPT-4, Claude, Gemini, or Llama thinks about your brand, your industry, or your expertise, what does it know — and how can you influence that?
The Definition Table
| Attribute | GEO | AEO | LLMO |
|---|---|---|---|
| Full name | Generative Engine Optimisation | Answer Engine Optimisation | Large Language Model Optimisation |
| Coined | 2023 (Princeton et al.) | ~2019 (industry usage) | ~2024 (industry usage) |
| Primary target | AI-generated responses | Direct answer systems | LLM training data and knowledge |
| Platforms addressed | ChatGPT, Perplexity, AI Overviews, Gemini | Featured snippets, voice assistants, AI Overviews | GPT models, Claude, Gemini, Llama |
| Scope | Broad (entity, content, technical, authority) | Narrow (content structure for direct answers) | Deep (influencing model knowledge) |
| Primary tactics | Entity optimisation, schema, authority content, citation engineering | Q&A formatting, featured snippet targeting, structured answers | Training data influence, entity consistency, cross-platform presence |
| Measurement | AI citation rate, share of voice | Featured snippet capture, direct answer inclusion | Model knowledge accuracy, brand representation |
| Closest traditional equivalent | SEO (but for AI search) | Featured snippet optimisation | Brand reputation management (but for AI) |
| Academic backing | Strong (Princeton paper, subsequent research) | Moderate (industry-developed) | Emerging (primarily practitioner-defined) |
| Industry adoption | High (most widely used term in 2026) | Moderate (established but narrower) | Lower (more technical, less accessible) |
Where They Overlap
The three terms are not entirely distinct. They share common elements:
All three care about structured content. Whether you call it GEO, AEO, or LLMO, content that is well-structured, data-rich, and directly answers questions is more likely to be surfaced by AI systems.
All three require entity signals. For any AI system to cite, answer with, or know about your brand, it needs to recognise your brand as a distinct entity. Entity optimisation underpins all three approaches.
All three benefit from schema markup. Structured data helps every type of AI system understand and cite your content.
All three involve cross-platform presence. AI models draw from multiple sources. Consistency across Wikipedia, industry databases, your own website, and third-party publications supports all three.
The overlap is significant enough that a well-executed GEO programme will, in practice, deliver most of the outcomes that AEO and LLMO separately promise.
Where They Diverge
The differences are real, even if they are more about emphasis than kind:
| Focus Area | GEO | AEO | LLMO |
|---|---|---|---|
| Featured snippets | Secondary concern | Primary concern | Not directly addressed |
| Voice search optimisation | Included but not central | Central focus | Not directly addressed |
| Training data influence | Part of the strategy | Not typically addressed | Primary focus |
| Citation engineering | Core activity | Not a primary focus | Indirect (better data = better citations) |
| Knowledge Graph development | Core activity | Beneficial but not central | Core activity |
| Content formatting for direct answers | Important | Critical | Less relevant |
| Cross-model consistency | Important | Less relevant | Critical |
| Time horizon | Medium-term (weeks to months) | Short-term (days to weeks for snippets) | Long-term (training cycles) |
The Practical Reality in 2026
Here is what matters for UK businesses right now: GEO has become the de facto umbrella term that encompasses the most important elements of AEO and LLMO. When a specialist agency talks about GEO in 2026, they are typically addressing:
- AI citation optimisation (the original GEO focus)
- Direct answer optimisation (the AEO focus)
- Model knowledge influence (the LLMO focus)
- Entity and authority architecture (shared across all three)
- Technical implementation and schema (shared across all three)
This convergence is not because AEO and LLMO are irrelevant — it is because a comprehensive GEO programme naturally includes their key activities.
At MarGen, our Synaptic Authority Engine addresses all three dimensions under the GEO framework:
- Citation engineering (GEO core)
- Answer formatting and direct response optimisation (AEO elements)
- Entity signal building and training data influence (LLMO elements)
Which Term Should You Use?
For clarity in strategic discussions, here is a practical guide:
| Context | Recommended Term |
|---|---|
| Discussing AI search visibility broadly | GEO |
| Talking to a traditional SEO team about featured snippets | AEO |
| Technical discussion about model training data | LLMO |
| Agency brief or RFP | GEO (most widely understood) |
| Board-level strategy discussion | GEO or “AI search optimisation” |
| Evaluating specialist agencies | GEO (and check they cover AEO and LLMO elements) |
The Terminology Trap to Avoid
The biggest risk of the terminology confusion is buying the wrong service. Some agencies position themselves as “AEO specialists” but only optimise for featured snippets — missing the broader AI citation landscape. Others claim “LLMO expertise” but focus on content generation rather than genuine entity and authority engineering.
When evaluating providers, focus less on which term they use and more on what they actually deliver. A comprehensive programme should include:
- Entity optimisation and Knowledge Graph development
- Structured content creation for AI citation
- Schema and structured data implementation
- Cross-platform authority building
- AI citation monitoring and reporting
- Featured snippet and direct answer optimisation
- Training data influence through entity consistency
If a provider covers all of these, the label they use matters far less than the results they achieve.
The Verdict
GEO, AEO, and LLMO describe different facets of the same strategic challenge: ensuring your brand is visible, accurate, and authoritative across AI-powered search and answer systems. GEO is the broadest and most widely adopted term. AEO is narrower but still relevant for direct answer optimisation. LLMO is the most technical and addresses the deepest layer of the stack.
For most UK businesses, a comprehensive GEO programme that includes AEO and LLMO elements is the right approach. The terminology matters less than the coverage.
Confused about which approach your business needs? Book a free GEO audit and we will assess your visibility across all AI search dimensions — citations, direct answers, and model knowledge — and recommend a programme that covers the lot.