What is Generative Engine Optimization (GEO)? A category definition
Generative Engine Optimization is the practice of making your brand visible inside answers generated by ChatGPT, Claude, Perplexity, and Google AI Overviews. It has an academic origin, a measurable 40 percent visibility lift in the original research, and a working definition most agencies still get wrong. The industry is still arguing about whether to call it GEO, AEO, LLM SEO, or AI SEO. The brands taking real share inside AI answers stopped arguing 18 months ago and started doing the work. This post defines the category, explains where the term came from, and tells you what to call it internally.
Where the term came from
GEO is not a marketing coinage. It comes from a 2023 research paper titled GEO: Generative Engine Optimization by Aggarwal, Murahari, Rajpurohit, Kalyan, Narasimhan, and Deshpande, affiliated with Princeton, Georgia Tech, and the Allen Institute for AI. The paper went up on arXiv as 2311.09735 and was later published at KDD 2024. The headline claim: the techniques produced up to 40 percent visibility boost inside generative engines, measured across 10,000 search queries. That number is what made the category stick.
The working definition
Strip out the academic framing: GEO is the set of content, technical, and distribution decisions that make a brand more likely to appear inside an AI-generated answer. It is measured by brand mention rate, citation rate, share of voice, and answer sentiment across a fixed prompt set. That is the entire discipline.
What separates it from SEO is the unit of ranking. SEO ranks URLs inside a list of ten blue links. GEO ranks brand mentions and quoted passages inside a synthesized paragraph. A page can rank number one in Google for a keyword and still not appear in the AI Overview for that query. That gap is the reason the category exists.
GEO is not a rebrand of SEO. It is optimization for a different unit of ranking.
Soar, GEO framework
GEO vs AEO vs LLM SEO vs AI SEO
Four acronyms. Same work. Different constituencies.
GEO (Generative Engine Optimization) is the academic term. It carries the research provenance, and Search Engine Land's 2025 industry survey found that 84 percent of marketers recognize it as the category label (Search Engine Land). It is the default among agencies, academics, and tool vendors who want a defensible name with a citation behind it.
AEO (Answer Engine Optimization) is the product-marketing term. Profound and HubSpot favor AEO (HubSpot's free measurement tool is called the AEO Grader). The Search Engine Land survey found 56 percent marketer recognition for AEO. Inside vendor-led conversations, you will hear AEO more often than GEO because the big vendors standardized on it early.
LLM SEO is what SEO veterans prefer. It treats the category as a direct extension of search optimization and keeps in-house SEO teams inside their comfort zone. When an SEO director says "LLM SEO," they mean GEO.
AI SEO is the catch-all for non-specialists. You see it in LinkedIn posts, board decks, and tool marketing. The January 2026 eMarketer briefing noted that the four terms are "still used interchangeably," and the Digiday explainer reached the same conclusion. Conductor's November 2025 benchmark wrote "AEO/GEO" as a compound term. Our position: GEO is the best name because it has the research citation, but use whichever term the client already has in their decks. Fighting about the acronym costs you the first two weeks of an engagement.
Why the category has a real technical identity
A fair critique of GEO is that it looks like SEO wearing a new hat. The critique is wrong. Three differences:
- The source pool. Traditional search optimizes for content on your own domain. GEO optimizes for whatever the model learned from: Reddit, Wikipedia, YouTube transcripts, Stack Overflow, press. You do not control most of those surfaces. You have to work inside them.
- The retrieval mechanism. AI answers come from some mix of training data, retrieval-augmented generation, and real-time search. Each engine uses a different mix. Interventions that move ChatGPT do not move Perplexity. The full technical explainer is in how LLMs decide what to cite.
- The measurement stack. SEO measures rank, clicks, and conversions on your own domain. GEO measures brand mention rate, citation rate, share of voice, and answer sentiment across a fixed prompt set. Newer tools, shakier baselines, different discipline.
Common misconceptions
Three mistakes show up on almost every first-time engagement.
GEO is SEO with new keywords. It is not. Keywords are secondary. Prompts are primary. A prompt like "what is the best payroll software for a 50-person agency" has no single keyword equivalent, and the signal set that makes you appear in the answer is not the signal set that ranks you for "best payroll software."
You can buy your way in with schema.org markup. You cannot. Google's own documentation says there is no special schema required for AI Overviews or AI Mode. Structured data helps at the margin. It is not a silver bullet. The full breakdown is in the 2026 guide to Generative Engine Optimization.
One good piece of content will get you cited everywhere. It will not. Engines weight different sources for different prompt types. What Perplexity loves (GitHub, Reddit, editorial) is not what ChatGPT loves (Bing-indexed pages, Wikipedia, training corpus). Real programs run per-engine interventions.
Who owns the term today
Three groups shape the definition. Academics own the original framing through the Princeton and Allen Institute paper. Tool vendors (Profound, HubSpot, Semrush, Ahrefs, Otterly, Peec) own the product-marketing layer and their vocabulary trickles down into the mid-market. Agencies own the services layer, and most real programs have converged on GEO as the client-facing term because it signals "we know the research" without starting an acronym fight.
What to call it inside your own organization
Pick one term and use it consistently. That is the rule. Whether it is GEO, AEO, LLM SEO, or AI SEO matters less than whether marketing, SEO, content, and leadership all use the same word. We have watched clients spend two full weeks, three deck rewrites, and a nine-person stakeholder meeting deciding whether to write "GEO" or "AEO" on a single slide.
If you have no preference, use GEO. It has the research citation, the highest recognition rate in surveys, and the clearest connection to the underlying work. If leadership already uses AEO because they bought a Profound or HubSpot license, use AEO. If your SEO team calls it LLM SEO, use LLM SEO. The work is the same. The vocabulary is a wrapper.
Conclusion
GEO is a real category with a real academic paper, a measurable effect, and a set of practices that differ meaningfully from SEO. The acronym debate is noise. The brands that stopped debating and started measuring are already showing up in ChatGPT, Claude, and Perplexity answers where their competitors are not. The gap between category-aware and category-blind brands widens every quarter, and no amount of vocabulary cleanup closes it.
How Soar saves you time and money
Most brands burn one to two weeks on internal terminology debate before the first audit runs. We skip that step. On day one we ask which term your leadership already uses in decks and board meetings, and we adopt it. If there is no preference, we default to GEO because of the research citation. The engagement starts on the work instead of on the wrapper.
Soar has been running AI visibility programs since before the category had a published name. We built the prompt-set templates, the measurement stack, and the per-engine intervention playbooks across hundreds of engagements. The first audit report lands within 14 days, the first intervention ships by day 30, and the monthly reporting cadence is standardized from month one. No stalled kickoff, no wasted months picking the wrong tool, no rehiring an SEO lead into a GEO role they are not ready for.
If you want a 30-minute walkthrough of where your brand stands on GEO (or AEO, or whatever you call it), request a proposal. We will run your top 20 prompts through Parse and tell you which engine is the biggest single opportunity.
Related reading
- The 2026 guide to Generative Engine Optimization
- How LLMs decide what to cite: training, retrieval, and real-time search
- The 90-day GEO program: from audit to first citations
- LLM SEO: how to rank on ChatGPT and Claude
- How to audit your brand's AI search visibility
- 12 best generative search optimization tools