Generative Engine Optimization (GEO)

Generative engine optimization (GEO) is the practice of making a brand's content and information easy for AI systems such as ChatGPT, Google AI Overviews, Perplexity and Microsoft Copilot to find, trust and cite when they generate an answer.

Classic SEO competes for a position in a list of links. GEO competes for a place inside the answer itself: being named as a recommended option, being quoted, or being listed as a source. When a buyer asks an assistant "which agency should we hire for X" or "what is the best tool for Y", the assistant writes a short answer from a handful of sources. GEO is the work of becoming one of those sources and being described accurately.

The term comes from a 2023 research paper by Aggarwal and colleagues (GEO: Generative Engine Optimization), which tested how content changes affect whether a source shows up in generative engine responses. It found that adding citations, quotations from relevant sources and statistics improved source visibility, while keyword stuffing did not.

You will also see GEO called GEO SEO, AI search optimization, AI SEO or LLM optimization (LLMO). Answer engine optimization (AEO) is a closely related label that grew out of featured snippets and voice search.

Most AI search features do not answer from memory alone. They run searches, read a small set of pages, and write a summary with links. Every step is a place where your content can be picked up or dropped.

1. Interpret and expand the question. The system rewrites the prompt into several searches. Google describes this "query fan-out" for AI Overviews and AI Mode, so one question can trigger related sub-queries you never targeted directly.
2. Retrieve candidates from an index. Google's AI features draw on Google's index, Microsoft Copilot grounds web answers in Bing, and ChatGPT search fetches results with its own crawler and search partners. A page that is blocked, not indexed or hidden behind client-side JavaScript is usually not in the candidate set.
3. Pick passages, not pages. The system extracts short segments that answer a sub-question. A clear, self-contained paragraph or table row is easier to lift than an answer spread across a long introduction.
4. Check agreement. Claims that several independent sources agree on are safer to repeat. A brand described the same way on its own site, in directories, reviews, forums and the press is easier to name with confidence.
5. Write and attribute. The answer is generated and some sources are cited or linked. Being retrieved does not guarantee a visible citation, so measurement looks at both mentions and citations.

Google states that there are no special requirements to appear in AI Overviews or AI Mode beyond being indexed and eligible to show a snippet, and that normal SEO best practices apply (Google Search Central: AI features and your website). That is why GEO starts with SEO foundations rather than replacing them.

GEO and SEO share the same foundation but aim at different outcomes. The short version:

In practice, yes. "GEO SEO" is how many teams describe adding generative engine optimization to an existing SEO program: the same site, the same technical hygiene, plus new goals (mentions and citations), new content formats (answer-first passages, tables, definitions) and new reporting (prompt tracking across AI systems).

Two cautions. First, "geo SEO" is also used for geo-targeted or local SEO, which is about physical location and map results, not generative engines. Check which one a vendor means. Second, treating GEO as a separate silo from SEO usually backfires: content written only for AI extraction, or pages generated at scale to "cover prompts", tend to weaken the site that AI systems retrieve from.

• Not a guaranteed placement. No vendor controls what ChatGPT, Gemini or Copilot says. Answers vary between runs, users and days, which is why GEO is measured with samples over time.
• Not keyword stuffing for AI. The original GEO research found keyword stuffing among the weakest tactics. Clear, specific, well-sourced writing performs better.
• Not a single file or tag. A proposed file such as llms.txt is cheap to publish, but Google, OpenAI and Microsoft have not documented it as a citation signal. It does not replace indexable, well-structured pages.
• Not mass-produced pages. Hundreds of near-identical "AI-optimized" pages create duplicate, low-trust signals. See GEO failure modes.

1. Write down 20 to 50 buyer prompts. Use the questions sales and support hear, plus category comparisons ("best X for Y", "X vs Y", "is X worth it").
2. Get a baseline. Run them in ChatGPT, Google AI Overviews or AI Mode, Copilot and Perplexity. Note who is named, which pages are cited and what is wrong about your brand.
3. Fix eligibility. Check robots rules, indexation in Google and Bing, rendering without JavaScript, and canonical conflicts on the pages that should be cited.
4. Rewrite the pages that matter. Lead with a one-sentence answer, add a comparison table or a specific example, and remove filler that separates the question from the answer.
5. Close entity and corroboration gaps. Align your descriptions across your site, profiles and directories; pursue mentions in the publications and communities your category's answers already cite.
6. Re-run the prompts on a schedule and report changes per engine, not as one blended score.

NRLC.ai is led by Joel David Maldonado, who leads SEO and AI search at a global domain registrar: 1.2M Google AI-feature impressions and 198K Microsoft Copilot citations in six months. Our generative engine optimization services cover the audit, technical and entity fixes, citable content, third-party mentions and ongoing AI visibility tracking.

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