What is answer engine optimization? AEO, GEO and LLM SEO compared

Answer engine optimization (AEO) is the practice of getting a brand named and cited in the answers that AI assistants generate. Generative engine optimization (GEO) is the same practice under the name the original research paper gave it. LLM SEO is the industry's informal label for both, and the three are used interchangeably in 2026.

The terms came from different places and stuck at different times. None of them has a governing body, so nobody can tell you that your usage is wrong. What matters is the question underneath the vocabulary: which AI surface are you talking about, and can you tell whether you appear in it? That second half is what we do at RedClaw Labs, and it is described on AI visibility tracking.

The three terms side by side

Term	Where it came from	Which surface people mean when they use it
GEO (generative engine optimization)	Coined in an academic paper, Aggarwal et al., GEO: Generative Engine Optimization (arXiv 2311.09735, KDD 2024)	Any system that writes an answer from retrieved sources. The paper's own test engine was a reconstruction, not a shipped product
AEO (answer engine optimization)	Marketing vocabulary. The older of the three labels	Today, usually AI chat assistants. Some writing still uses it for Google's answer boxes, which is a source of confusion
LLM SEO	Informal, search-industry usage	The same work as the two above, framed as an extension of what an SEO team already does

The one distinction worth defending is that AEO is the older label, and some pages still use it in the older sense. On 2026-09-20 we read the pages then ranking for this term: one of them defines AEO entirely as a Google SERP tactic and never mentions ChatGPT. So a page that ranks for the term may be answering a question you are not asking. Check what surface a definition is about before you take it.

Which surface are you actually talking about?

This is the question the definition articles skip, and it is the one that decides what you can measure.

AI assistants are ChatGPT, Gemini, Perplexity and the rest. A person types a question, the assistant retrieves sources and writes a paragraph, and your brand either appears in that paragraph or does not. These have official APIs, which means the same question can be asked on a schedule and the answers compared week to week. This is the surface we measure.

Google AI Overviews and AI Mode sit on top of Google Search results. They are a different surface and are outside what we measure. Google publishes its own guidance for site owners there, and its position is that nothing new is required: the documentation states there are "no additional requirements to appear in AI Overviews or AI Mode" (Google Search Central, AI features and your website, retrieved 2026-09-20). We have no measurement of our own to add to that.

Treating the two as one surface is the most common mistake in writing about this topic. A vendor reporting "AI visibility" without saying which engines it queried has told you nothing you can act on.

What LLM visibility means

LLM visibility is how often an AI assistant names your brand when asked a question a buyer in your category would actually ask. It is a proportion, not a rank: mentions divided by valid answers, over a fixed set of questions that do not contain your brand name.

The last clause carries the weight. A question that names you produces an answer that names you every time, so a question set built around your own brand measures the question rather than the market. The full set of rules we use, including the metric definitions and the sample sizes behind them, is on our methodology page.

How the work differs from SEO

	Search engine optimization	Optimizing for AI answers
What you win	A position in a list of links	A mention, and sometimes a citation, inside written prose
How results are checked	Rank for a keyword, broadly stable between checks	A proportion across repeated questions, different on each asking
Unit of measurement	Position 1 to 10	Mention rate with a confidence interval
Who the competition is	Whoever ranks on the same query	Whoever the model names in the same answer, including brands that outrank nobody
The main failure mode	Not ranking	Ranking, being read, and still not being named

The last row is the useful one. Pages get retrieved and summarised without the brand behind them surviving into the answer text, which is a category of problem that rank tracking cannot see.

On the optimization side, the honest state of the evidence is thinner than the vocabulary suggests. The GEO paper tested nine content edits against a benchmark of 10,000 queries and found that "the best methods improve upon baseline by 41% and 28%" on its two visibility metrics, with adding quotations and adding statistics near the top (Aggarwal et al., arXiv 2311.09735, retrieved 2026-09-20). Read the setup before you act on the number: the study's generative engine was assembled by the authors from the top five Google results plus gpt-3.5-turbo, not the assistant on your phone, and the paper itself reports that the gains vary by domain. It is good evidence that content edits move citation behaviour. It is not a tuning guide for a 2026 product.

Why one answer is not a measurement

On 2026-09-20 we ran one measurement through our own pipeline, using the ChatGPT API with web search. The brand watched was Figma, the category was UI design and prototyping tools, the market was US. The pipeline generated three buyer questions and asked each of them once:

1. "Can you recommend UI design and prototyping tools that are best suited for small design agencies in the US with limited budgets?"
2. "Which UI design and prototyping tools offer the most seamless collaboration features for remote teams based in the US?"
3. "What are the top UI design and prototyping tools that provide fast learning curves for US-based freelance designers needing quick project turnarounds?"

Figma was named in 3 of 3 answers, which is what we expected from a category leader and is mainly a check that the brand matching works. The interesting column is the rest of the list:

Brand named alongside Figma	Answers naming it, out of 3
Sketch	3
Framer	3
ProtoPie	2
Principle	2
Adobe XD	2

Three questions about one category, asked on one day, of one engine, produced three different sets of rivals. Budget framing pulled in one group of names; the collaboration and learning-curve framings pulled in others. Nothing about the market changed between the questions. Only the wording did.

Two things follow, and it is worth keeping them apart.

The first is measured here: how the question is phrased changes who appears next to you. A competitive set read off a single question is a description of that question.

The second is not measured here, and we are not going to pretend otherwise. This run asked each question once, so it says nothing about how much a single question varies when you ask it again. That variation is real, and it is why our paid monitoring asks every question three times per engine and reports a confidence interval rather than a percentage. But the number above is a single draw per question, and a single draw cannot establish its own error bar. The reasoning behind the sample sizes, and what they can and cannot support, is set out on our methodology page. We keep the raw output of every published run, including the full answer text and the pipeline record it came from, so that any figure here can be traced back to the answer it was counted from.

Questions

Is AEO the same thing as GEO? In current usage, yes. GEO came from a 2023 research paper and AEO from marketing vocabulary, and both now describe getting named in AI-generated answers. Only AEO causes trouble, because some writing still uses it for Google's own answer boxes rather than for chat assistants.

How is LLM SEO different from traditional SEO? The goal changes from occupying a position in a list to being named inside a written answer, so the measurement changes from a rank to a proportion across repeated questions. Much of the underlying work overlaps, because an assistant cannot cite a page it cannot retrieve.

What is LLM visibility? The share of AI answers to a fixed set of buyer questions in which your brand is named. Reported properly it comes with the question set, the engines queried, the number of answers behind it, and a confidence interval.

Do you measure Google AI Overviews? No. AI Overviews and Google's AI Mode are outside what we measure. Our monitored surfaces are ChatGPT, Gemini and Perplexity, through their official APIs.

Why does ChatGPT name different brands each time I ask? Partly because these models sample rather than look up a fixed table, and partly because small changes in how the question is framed retrieve different sources, as the run above shows. It is the normal behaviour of the system, not a fault, and it is the reason a single answer is an anecdote.

Which term should I use in my own documentation? Pick one and define it on first use. The vocabulary is unsettled enough that the definition does more work than the label, and a reader who knows which surface you mean will not care which of the three words you chose.

What to do next

Decide which surface you care about before you buy anything that claims to optimize for it. If the answer is AI assistants, the first useful step is not a content rewrite but a baseline: a question set written in buyer language with your brand name kept out of it, asked often enough that you can tell a real change from the noise. How we build that baseline, what it costs and what it does not cover is on AI visibility tracking.