---
title: "GEO vs SEO vs AEO: what actually changed"
subtitle: "Where the three terms come from, what they share, and the three things that genuinely differ when the reader is a language model."
canonical: https://citable.wiki/guides/geo-vs-seo-vs-aeo
category: fundamentals
author: "Endrit Krasniqi"
date_published: 2026-01-19
date_modified: 2026-01-19
license: CC BY 4.0
---

# GEO vs SEO vs AEO: what actually changed

*Where the three terms come from, what they share, and the three things that genuinely differ when the reader is a language model.*

## Short answer

Search Engine Optimization (SEO), Answer Engine Optimization (AEO) and Generative Engine Optimization (GEO) describe three stages of the same job. SEO optimises a page to rank in a list. AEO optimises a passage to be extracted as a direct answer. GEO, from a 2023 paper, optimises a source to be cited inside an answer written by a language model. Crawlability and quality carry over; the unit, reward and pipeline change.

## Key takeaways

- SEO, AEO and GEO share one foundation: a crawlable, indexable page with verifiable quality signals. None of the newer terms removes that requirement.
- GEO was named in a November 2023 research paper by Aggarwal et al.; AEO is an older industry term with no single origin.
- The unit of optimisation moved from the page to the passage, and the reward moved from a click to a citation.
- Answer engines run a retrieval pipeline — crawl, chunk, retrieve, ground, generate — and each stage rewards different properties of your content.
- Google, OpenAI, Anthropic and Perplexity all document separate crawlers for search, user requests and model training; allow them separately in robots.txt.
- Prioritise SEO fundamentals first, AEO structure second, and GEO evidence tactics third; the order is fixed, the effort split is not.

## Three names for one shift

Search Engine Optimization, Answer Engine Optimization and Generative Engine Optimization are usually presented as rivals. They are better read as a timeline. Each term was coined when the system on the other end of the query changed, and each inherits almost everything from the one before it.

The short version: **SEO** optimises a page to rank in a list, **AEO** optimises a passage to be extracted as an answer, and **GEO** optimises a source to be cited inside an answer that a language model has written. This guide places each term, separates what carries over from what does not, and ends with a decision guide for where to spend effort.

## Where each term came from

### SEO: the page and the ranking

Search Engine Optimization dates from the mid-1990s and has a stable meaning: make a URL discoverable, indexable and competitive for a query so that it ranks high in a list of results. Its signals are well documented — crawlability, relevance, links, page experience — and Google publishes most of the guidance that defines the field on Search Central. The reader is a human who scans ten links and picks one.

### AEO: the passage and the extraction

Answer Engine Optimization is an industry term, not an academic one, and it has no single origin. SEO practitioners were using it before generative AI to describe the work of winning featured snippets, "People also ask" boxes and voice-assistant answers — all systems that return one passage instead of a list. After ChatGPT's release in late 2022 the term was widely adopted for AI assistants as well. The [glossary entry](/glossary/answer-engine-optimization) gives the working definition this site uses, and [What is Answer Engine Optimization?](/guides/what-is-answer-engine-optimization) walks through the tactics.

### GEO: the source and the citation

Generative Engine Optimization has a precise origin. The paper *GEO: Generative Engine Optimization* by Pranjal Aggarwal, Vishvak Murahari and colleagues was posted to [arXiv on 16 November 2023](https://arxiv.org/abs/2311.09735) and later presented at KDD 2024. It defines a **generative engine** as a system that synthesises an answer from multiple retrieved sources with a large language model, proposes a benchmark called GEO-bench, and measures which edits to a source increase its share of the generated answer.

**Up to 40%** — increase in a source's visibility in generative engine responses from the best-performing content edits, as reported in the paper's abstract (Source: [GEO: Generative Engine Optimization (Aggarwal et al., 2023)](https://arxiv.org/abs/2311.09735))

Its most repeated finding is that edits which added citations, quotations from authorities and statistics improved visibility, while stuffing in extra keywords did not help. That result is the practical dividing line between GEO and SEO: evidence outperforms keyword coverage when the reader is a model.

> **Definition: Working definitions**
>
> - **SEO** — optimising a page to rank in a list of search results for a query.
> - **AEO** — optimising a passage so that any system which returns a direct answer can extract and use it.
> - **GEO** — optimising a source so that a language model, writing an answer from retrieved sources, uses and cites it.

## What stays the same

The newer terms attract a lot of claims that the fundamentals no longer apply. The vendors say otherwise.

Google's documentation on AI features states that "there are no additional requirements to appear in AI Overviews or AI Mode, nor other special optimizations necessary", and that no new machine-readable files or special schema.org types are needed. The same Googlebot that indexes for Search feeds AI Overviews and AI Mode, and the same `noindex`, `nosnippet`, `data-nosnippet` and `max-snippet` controls limit what those features can show.

Three things therefore carry over unchanged:

1. **Crawlability and indexing.** A page that cannot be fetched, or that is blocked for the relevant user agent, cannot be retrieved. OpenAI, Anthropic and Perplexity each publish the crawler names their search features use, and each says those crawlers respect `robots.txt`.
2. **Quality and E-E-A-T.** Google's guidance on helpful content says E-E-A-T "isn't a specific ranking factor", but that its systems use a mix of signals to identify experience, expertise, authoritativeness and trust. Answer engines lean on the subset of those signals a machine can check without reading for quality: a named author, visible dates, and links to primary sources.
3. **Semantic HTML and clean rendering.** Server-rendered HTML with a logical heading outline was good practice for Googlebot and is now the difference between a passage that chunks cleanly and one that does not.

If a page does not rank at all, AEO and GEO tactics have nothing to work with. That is the single most useful thing to know about the three terms.

## What actually changed

Three things did change, and they are structural rather than cosmetic.

### The unit is the passage

Classic ranking scores a URL. An [answer engine](/glossary/answer-engine) splits the page into chunks — usually by heading and paragraph, sometimes by a fixed token window — and scores each chunk against the question. A passage that opens mid-argument, or that refers to "it" without naming the entity, loses at this stage regardless of how strong the page is. Every chunk has to survive on its own.

### The reward is the citation

A ranking produces a click. A generative answer produces a **citation**: a footnote or inline link to the source the model used. The citation may or may not produce a click, and some engines send far less referral traffic than others. That changes what you measure — share of citations for a set of questions, not only sessions from organic search — and it changes what you write for, because the model is choosing which passage to attribute, not which page to list.

### The pipeline is retrieval, not ranking

Answer engines are built on [retrieval-augmented generation](/glossary/retrieval-augmented-generation): fetch candidate passages, ground the model in them, generate, attribute. Each stage rewards something different — being fetchable, chunking cleanly, matching the question's wording and entities, containing verifiable evidence, and being the answer rather than discussing it. SEO tactics map onto the first stage only; AEO and GEO tactics map onto the rest.

The pipeline also changed the crawler landscape. OpenAI's crawler documentation lists `OAI-SearchBot` for ChatGPT's search features, `ChatGPT-User` for user-initiated fetches and `GPTBot` for model training. Anthropic's support documentation lists `Claude-SearchBot`, `Claude-User` and `ClaudeBot` with the same three roles, and Perplexity documents `PerplexityBot` for search and `Perplexity-User` for user actions. Google uses Googlebot for AI features; `Google-Extended` is a separate robots.txt token that limits AI training and grounding in some of its other systems. Being cited and being trained on are now separate decisions, and `robots.txt` can express them separately:

```text
# Allow search and user-initiated fetches; decline model training.
User-agent: OAI-SearchBot
User-agent: ChatGPT-User
User-agent: Claude-SearchBot
User-agent: Claude-User
User-agent: PerplexityBot
Allow: /

User-agent: GPTBot
User-agent: ClaudeBot
User-agent: Google-Extended
Disallow: /
```

Two caveats from the vendor pages. OpenAI and Perplexity both note that their user-initiated fetchers (`ChatGPT-User`, `Perplexity-User`) act on behalf of a person and may not apply `robots.txt` rules. And `Google-Extended` does not affect AI Overviews or AI Mode; those follow the ordinary Googlebot rules.

## Side by side

| Aspect | SEO | AEO | GEO |
| --- | --- | --- | --- |
| Origin | Industry, mid-1990s | Industry, no single origin; pre-dates generative AI | Aggarwal et al., arXiv, November 2023 |
| Target system | Ranked list of links | Anything that returns one direct answer: snippets, voice, AI chat | Engines that generate an answer with an LLM and cite sources |
| Unit optimised | Page (URL) | Passage | Source, as used within a generated answer |
| Reward | Ranking position and click | Extraction as the answer | Citation and share of the answer |
| Core signals | Crawlability, relevance, links, page experience | Answer-first structure, one question per URL, entity clarity, structured data | Citations, quotations, statistics, verifiable authorship |
| Measured by | Rank, impressions, clicks | Snippet and answer ownership | Citation frequency across a question set |
| Still needs | — | Everything SEO needs | Everything SEO and AEO need |

## Which one to prioritise

The terms stack, so the order is fixed even though the effort split is not. Work through them in sequence and stop at the first gap.

1. **Start with SEO if the page is not indexed or does not rank for its core query.** No answer engine will retrieve a passage from a page it cannot fetch or does not trust. Fix crawl access, rendering, canonicals and thin content first.
2. **Move to AEO once the page ranks but is paraphrased rather than quoted.** Put a 40–70 word direct answer at the top, give each question its own URL and heading, name entities in full, and emit `Article`, `FAQPage` and `Person` markup from the same fields as the visible text.
3. **Move to GEO once passages are extracted but rarely cited.** Add what the GEO paper found generative engines reward: outbound citations to primary sources, quotations from named authorities and concrete statistics with a source. Check crawler access for the search-mode user agents above.
4. **Measure at every stage in the unit that stage rewards.** Rank for SEO, snippet or answer ownership for AEO, and citation share across a fixed question set for GEO.

**Decision guide: which term is your bottleneck**

- [ ] The page is not indexed, or is blocked for Googlebot, OAI-SearchBot, Claude-SearchBot or PerplexityBot: SEO and crawler access
- [ ] The page ranks, but no section answers a specific question in its first 70 words: AEO
- [ ] Sections answer questions, but headings do not match how people ask them: AEO
- [ ] Passages appear in AI answers paraphrased but uncredited: GEO
- [ ] Passages are cited by one engine but not others: check per-engine crawler rules, then GEO
- [ ] Author, dates and sources are missing or not marked up: E-E-A-T, which all three share

The names matter less than the sequence. A team that ships crawlable pages, answer-first passages and cited evidence is doing SEO, AEO and GEO at once, whatever it calls the work.

> **Tip: Where to go next**
>
> [How to write answer-first content](/guides/how-to-write-answer-first-content) covers the AEO layer — the passage structure that survives chunking. [Controlling AI crawlers with robots.txt](/guides/controlling-ai-crawlers-with-robots-txt) covers the access layer, including the per-engine split between search, user and training crawlers.

## Frequently asked questions

### Is GEO just a new name for SEO?

No, but it depends on the same foundation. SEO optimises a page to rank in a list of links. GEO optimises a source so that a generative engine uses and cites it when writing an answer. The crawlability, indexing and quality work is shared; what differs is that GEO rewards self-contained passages with citations, quotations and statistics rather than keyword coverage and link equity.

### Where does the term GEO come from?

From the paper GEO: Generative Engine Optimization by Pranjal Aggarwal and colleagues, first posted to arXiv on 16 November 2023 and later presented at KDD 2024. It defined generative engines as systems that synthesise an answer from multiple sources with a language model, introduced the GEO-bench benchmark, and reported that some content edits raised a source's visibility by up to 40%.

### What is the difference between AEO and GEO?

AEO is the broader, older term: optimising for any system that returns a direct answer, including featured snippets, voice assistants and AI chat. GEO is narrower and academic: optimising for engines that generate the answer with a language model and cite sources. In practice the tactics overlap almost completely, and most teams use whichever term their stakeholders recognise.

### Should I stop doing SEO and switch to AEO or GEO?

No. Every answer engine that cites the web discovers pages through crawling and filters them with quality signals that classic SEO already covers. Google states there are no additional requirements to appear in AI Overviews or AI Mode. Treat AEO and GEO as a layer added to a page that already ranks, not as a replacement for the work that got it there.

### Does E-E-A-T still matter for answer engines?

Yes, and arguably more. Google's helpful-content documentation says E-E-A-T is not itself a ranking factor, but that its systems look for signals of experience, expertise, authoritativeness and trust. A retrieval system cannot judge quality by reading, so it leans on what it can verify: a named author, visible dates, and outbound links to primary sources.

## Sources

1. [GEO: Generative Engine Optimization](https://arxiv.org/abs/2311.09735) — Aggarwal et al., KDD 2024 (arXiv) (2023)
2. [AI features and your website](https://developers.google.com/search/docs/appearance/ai-features) — Google Search Central (2025)
3. [Creating helpful, reliable, people-first content](https://developers.google.com/search/docs/fundamentals/creating-helpful-content) — Google Search Central (2025)
4. [Overview of OpenAI crawlers](https://developers.openai.com/api/docs/bots) — OpenAI (2025)
5. [Does Anthropic crawl data from the web, and how can site owners block the crawler?](https://support.claude.com/en/articles/8896518-does-anthropic-crawl-data-from-the-web-and-how-can-site-owners-block-the-crawler) — Anthropic (2025)
6. [Perplexity crawlers](https://docs.perplexity.ai/guides/bots) — Perplexity (2025)

## Related guides

- [What is Answer Engine Optimization (AEO)?](https://citable.wiki/guides/what-is-answer-engine-optimization)
- [How to write answer-first content that LLMs can quote](https://citable.wiki/guides/how-to-write-answer-first-content)
- [Controlling AI crawlers with robots.txt: GPTBot, ClaudeBot, PerplexityBot and friends](https://citable.wiki/guides/controlling-ai-crawlers-with-robots-txt)
- [E-E-A-T for answer engines: authors, dates and sources that machines can verify](https://citable.wiki/guides/eeat-for-answer-engines)

---

Source: https://citable.wiki/guides/geo-vs-seo-vs-aeo
Author: Endrit Krasniqi
More: https://citable.wiki/llms.txt