What is Generative Engine Optimization (GEO)?

Key takeaways

• Generative engine optimization (GEO) means improving the chance that AI answer engines retrieve, trust and cite your content.
• The term comes from a 2023 research paper by Aggarwal et al., which found that adding citations, quotations and statistics raised source visibility, while keyword stuffing did not.
• GEO builds on SEO rather than replacing it. If a page cannot be crawled and indexed, it cannot be cited.
• The core techniques are answer-first passages, structured data, consistent entities, sourced statistics, clear crawler access and, optionally, an llms.txt file.
• Results are measured with prompt panels, citation tracking, AI referral traffic and server logs, never with guarantees.

What is generative engine optimization? A definition

Generative engine optimization (GEO) is the practice of structuring and publishing content so that generative AI systems, such as ChatGPT, Perplexity, Google AI Overviews, Gemini, Microsoft Copilot and Claude, can find it, understand it and cite it when they compose an answer.

A traditional search engine returns a ranked list of links and leaves the reading to the user. A generative engine reads for the user. It retrieves several sources, synthesizes one answer and, in most products, attaches citations. The goal of GEO, sometimes called generative search optimization, is to be one of those cited sources and to have your facts represented accurately.

Where GEO comes from: the 2023 research paper

The term was introduced in “GEO: Generative Engine Optimization”, a paper by Pranjal Aggarwal, Vishvak Murahari, Tanmay Rajpurohit, Ashwin Kalyan, Karthik Narasimhan and Ameet Deshpande, with researchers from Princeton University among the authors. It was first posted on arXiv in November 2023.

The paper did three things:

1. It framed the problem: content creators have little visibility into, or control over, how generative engines use their pages.
2. It released GEO-bench, a benchmark of queries across many domains, so that optimization methods could be compared.
3. It tested specific methods, such as citing sources, adding quotations, adding statistics, improving fluency and keyword stuffing, and measured how each changed a source’s visibility in generated answers.

The authors report that the best methods improved visibility by up to 40% on their benchmark. Adding citations, quotations and statistics performed well, and classic keyword stuffing performed poorly. These are research results on a benchmark, not a promise about any commercial engine, but they point in a sensible direction: evidence and clarity beat repetition.

GEO vs SEO: what changes and what does not

	SEO	GEO
Goal	Rank a page in a results list	Be retrieved and cited inside a generated answer
Unit of competition	The whole page	The passage or fact that answers a sub-question
Success signal	Position, clicks, impressions	Citations, brand mentions, accuracy of the answer, AI referrals
Key inputs	Crawlability, relevance, links, page experience	All of SEO, plus quotable passages, sourced facts and entity clarity
User behavior	Scans links and clicks	Reads the answer, clicks a citation only when needed
Measurement maturity	Established tools and consoles	Emerging, mostly sampled and manual
Volatility	Rankings shift over weeks	Answers can change between two runs of the same prompt

The overlap is large. Most answer engines retrieve from a web index, and pages that are slow, blocked or thin rarely make it into that pool. GEO vs SEO is not a choice. GEO is SEO with an extra layer aimed at how machines read and quote.

How answer engines pick their sources

No answer engine publishes its selection formula, so be wary of anyone who claims to know it. In general terms, most systems follow a retrieval-augmented generation pattern:

1. Interpret the prompt. The engine may split it into several sub-queries.
2. Retrieve candidates. It runs searches against a web index, its own or a partner’s, and pulls relevant pages or passages.
3. Select and rank passages. It favors text that directly answers a sub-query, comes from a source that looks reliable, and agrees with other sources.
4. Generate and cite. The model writes an answer and links the sources it relied on.

Three practical conclusions follow. Your page must be reachable and indexable. Individual passages must stand on their own when lifted out of context. And your facts should be corroborated elsewhere on the web, because a claim that only you make is harder for a system to trust.

Practical GEO techniques

Answer-first passages

Open each page and each major section with a direct, self-contained answer of two or three sentences, then expand. Use descriptive ## headings phrased the way people ask questions. A passage that still makes sense when quoted alone is a passage an engine can use.

Structured data

Add JSON-LD using schema.org types that match the page: Organization, LocalBusiness, Service, Product, Article, FAQPage, BreadcrumbList. Google has narrowed which sites get FAQ rich results since 2023, but structured data still states who you are and what the page covers in a machine-readable form.

Entity consistency

Use the same company name, description, address, founding year and service names everywhere: your site, social profiles, directories and partner pages. Link them with sameAs in your Organization markup. Inconsistent entities make it harder for a system to be confident that two mentions refer to you.

Statistics with sources

Specific, checkable numbers (“68 pages”, “3–6 weeks”) are more quotable than adjectives. When you cite external figures, name the source and link it. Never invent a statistic. If an engine repeats it, the error now carries your name.

llms.txt

llms.txt is a proposal published by Jeremy Howard in 2024: a Markdown file at /llms.txt that summarizes a site and links its most useful pages for language models, often with a fuller llms-full.txt companion. It costs little to add. Adoption by major answer engines has not been publicly confirmed, so treat it as a courtesy to AI tools, not a ranking factor.

Crawler access

Check robots.txt, your CDN and your firewall rules. Several AI companies run separate agents for model training and for search or live retrieval:

Company	Training crawler	Search / retrieval agents
OpenAI	GPTBot	OAI-SearchBot, ChatGPT-User
Anthropic	ClaudeBot	Claude-SearchBot, Claude-User
Perplexity	—	PerplexityBot, Perplexity-User
Google	Google-Extended (a control token, not a separate crawler)	Googlebot, which also feeds AI Overviews

Blocking a training crawler is a legitimate policy choice. Blocking search agents usually removes you from that engine’s answers. Make the decision deliberately, page by page if needed.

How to measure GEO

GEO measurement is younger than SEO measurement. A workable setup combines four signals:

• Prompt panel. Write 30–100 prompts your buyers actually ask, run them on a fixed schedule in each engine, and log whether you are mentioned, linked and described accurately. Run each prompt more than once, because answers vary.
• Citation share. For each topic, track how often your domain appears among cited sources compared with competitors.
• AI referral traffic. Segment analytics sessions that come from AI assistants (for example chatgpt.com or perplexity.ai) and follow them through to conversions.
• Server logs. Count hits from AI search agents. Rising crawl activity on the right pages is an early, if indirect, signal.

Expect AI referrals to be smaller in volume than classic organic traffic and often higher in intent. Judge GEO over quarters, not weeks.

Common GEO myths

• “GEO replaces SEO.” It does not. Most engines retrieve from web indexes, so SEO fundamentals remain the entry ticket.
• “llms.txt gets you cited.” It is a proposal with unconfirmed adoption. Useful, not decisive.
• “You can buy placement in ChatGPT answers.” Organic citations are not for sale. Anyone selling guaranteed mentions is selling something else.
• “Stuff the prompt keywords into the page.” The original GEO research found keyword stuffing did not improve visibility.
• “Schema markup alone is enough.” Structured data clarifies content but cannot substitute for content worth citing.
• “Only big brands get cited.” Engines often cite specialist pages that answer a narrow question better than a large site does.

How RAIN applies GEO

RAIN Design Studio builds GEO into the site itself rather than adding it afterwards. Every site we ship includes semantic HTML, JSON-LD, a sitemap, hreflang where relevant, llms.txt and llms-full.txt, and an AI-crawler-friendly robots.txt.

• Media Progetti: 68 French-language pages with JSON-LD on 66 of 69 pages, including FAQ markup on 59 and LocalBusiness markup on 12. The delivery statement reported a Semrush site audit of 100% site health and 100% AI search, with 0 errors across 78 crawled pages. That is a technical audit score, not a ranking result.
• Origin Element: 36 SEO/GEO content pages (services, regions, industries, guides), with llms.txt and llms-full.txt generated from the site’s own structured data so they never drift from the pages.

Both are Astro builds. Our guide to what Astro is explains why HTML-first output suits AI crawlers, and Astro web development covers how we build on it. For French-language search in Morocco, see our agence SEO au Maroc page.

Talk to us about GEO

If you want to know how answer engines currently describe your business, and what to fix first, book a free 15-minute call. Our generative engine optimization service runs as a fixed-scope project from $10,000 or within a Growth retainer at $7,500 per month. We report what we measure and promise no rankings.