What Is Generative Engine Optimization and How Does GEO Work

Generative engine optimization (GEO) is the practice of making a brand, product or page more likely to be mentioned, cited and described accurately inside answers written by AI engines such as ChatGPT, Gemini, Perplexity, Claude and Google’s AI Overviews and AI Mode. Classic SEO aims for a ranked blue link. GEO aims for a place inside the generated answer itself, either as a named recommendation or as a cited source.

The term comes from a 2023 research paper, and the discipline has grown into a working practice that mixes content, PR, technical access and measurement. This guide covers what GEO is, how the engines assemble answers, which levers actually move results, and how to measure progress without fooling yourself.

What is generative engine optimization, in plain terms?

A generative engine is any system that answers a question by writing new text, often grounded in web sources it retrieved for that question. When someone asks “best CRM for a 10-person agency”, the engine does not return ten links. It returns a paragraph or a list that names a handful of products and, depending on the engine, cites a few pages.

GEO is the work of influencing that answer. In practice it has three goals:

• Inclusion. Your brand appears in answers to the prompts your buyers actually ask.
• Attribution. Your pages are cited as sources, which earns a link and some referral traffic.
• Accuracy and tone. The engine describes you correctly (pricing, features, location) and in a favorable or neutral way.

You will also see the label “GEO SEO” used for the same work, because most of it builds on search fundamentals. Whether GEO is a separate discipline or an extension of SEO is a debate of its own, and the short answer is that the two overlap heavily but are measured differently.

Where the term GEO came from

The name was introduced in the paper GEO: Generative Engine Optimization, first posted to arXiv in November 2023 and published at KDD 2024. The authors were Pranjal Aggarwal of IIT Delhi, Vishvak Murahari, Karthik Narasimhan and Ameet Deshpande of Princeton University, plus Tanmay Rajpurohit and Ashwin Kalyan.

The researchers built a benchmark called GEO-bench with 10,000 queries across 25 domains. Their test engine was a simulation: it fetched the top five Google results for a query and had gpt-3.5-turbo write an answer from them. They then rewrote the source pages with nine different methods and measured how much of the answer each source “won”, using a position-adjusted word count.

The headline result was that the right edits could raise a source’s visibility in the answer by up to 40%. The table values tell a more useful story than the headline:

Two caveats matter. The engine was a lab setup, not live Google or ChatGPT, and the paper itself reports that which method works best varies by domain. Treat the findings as direction, not as a recipe that guarantees the same gains on today’s engines.

How does generative engine optimization work inside an AI engine?

To see how GEO works, you need a rough model of how an answer is built. Every engine differs in detail, but most follow the same four stages.

1. Deciding whether to search

A language model can answer from what it learned in training (its parametric memory) or it can retrieve fresh sources first. Google’s Gemini API documentation describes this openly: the model decides whether a search would help, generates one or more queries, synthesizes the results, and returns text with inline citations. Questions about recent events, prices or local options are far more likely to trigger a search than general knowledge questions.

2. Query fan-out and retrieval

When the engine does search, it rarely runs your exact prompt. Google has described AI Mode as using a “query fan-out” technique that splits a question into subtopics and runs many searches at once. Each sub-query pulls from an index: Google’s own index for AI Overviews and AI Mode, a mix of third-party search and partner content for ChatGPT search, and Perplexity’s own index for Perplexity. This is why a page can be cited for a prompt it does not rank for. Ahrefs found in March 2026 that only 37.9% of AI Overview citations came from pages ranking in the top 10 for the original query.

3. Grounding on passages, not pages

The engine does not read whole pages. It pulls passages. Dan Petrovic of DEJAN analyzed 7,060 Google grounding queries and found a median budget of about 1,929 words of source text per query, with the top-ranked source contributing around 531 words and the median page about 377. Long pages are not read in full, so the passage that answers the question has to stand on its own.

4. Synthesis and citation

Finally the model writes the answer, picks which brands to name and in what order, and attaches citations. Answers are not deterministic. In a SparkToro and Gumshoe study of 2,961 runs, the same brand list came back less than 1% of the time, even though the leading brands still appeared in most answers. GEO therefore deals in probabilities: you raise the odds of inclusion rather than lock in a fixed position.

The four levers of GEO

Most tactics you will read about fit into four levers. Each one affects a different stage of the answer pipeline above, which is a useful test for any new “GEO hack” you come across: if you cannot say which stage it changes, be skeptical.

On-page citability

Write so that a single passage can be lifted and still make sense. Put the direct answer in the first one to three sentences under each heading, use precise figures with their source and date, and name the people or studies behind claims. Kevin Indig reported that 44.3% of ChatGPT citations in his sample came from the first 30% of a page’s text, which supports front-loading the answer rather than building up to it.

Off-site mentions

Engines lean heavily on what other sites say about you. In an Ahrefs study of about 75,000 brands, branded web mentions correlated with AI Overview visibility at 0.664, while backlinks correlated at only 0.218 (Ahrefs, May 2025). Peec AI’s March 2026 analysis of 30 million citations put Reddit, YouTube, LinkedIn and Wikipedia at the top of the most-cited domains. Being discussed on the sites engines already trust often matters more than one more blog post on your own domain.

Technical access

An engine cannot cite what it cannot fetch. Each vendor runs separate bots for training and for search: OpenAI’s OAI-SearchBot, Anthropic’s Claude-SearchBot and PerplexityBot handle search inclusion, while GPTBot and ClaudeBot collect training data. Blocking the search bots can remove you from those answers or sharply reduce your presence in them. Most live AI fetchers also read raw HTML without running JavaScript, so content that only appears after client-side rendering may be invisible to them. For Google, AI features follow normal Googlebot rules; the Google-Extended token does not keep content out of AI Overviews or AI Mode.

Entity consistency

If your pricing page, G2 profile and LinkedIn page disagree, the engine has to guess, and it may guess wrong. Keep core facts identical everywhere: what you are, who you serve, what it costs, where you operate. Fixing a fact in a model’s training data is slow. Petrovic estimates roughly three to six months for fine-tunes and about a year for major model releases, so consistency is a long-term investment rather than a quick fix.

What Google says about GEO

Google’s position is that there is no separate playbook for its own AI features. Its AI features documentation says there are no additional requirements to appear in AI Overviews or AI Mode beyond being indexed and eligible for a snippet. A May 2026 Google guide goes further and lists llms.txt files, “chunking” content and writing special versions for AI as unnecessary for Google Search.

That does not make GEO pointless. It means that for Google surfaces, GEO mostly is good SEO plus measurement. The picture differs for engines that use other indexes. ChatGPT citations, for example, have matched Bing’s top results more closely than Google’s in third-party tests. Off-site reputation also shapes answers in ways that ranking work alone does not touch. A sensible GEO program does the SEO basics well and then adds the parts SEO never measured: mentions, citations and how the brand is described.

How to measure GEO results

Measurement is where most GEO programs fail, because a single manual check of ChatGPT tells you almost nothing. Answers vary by run, by engine, by location and over time. The University of St. Gallen paper “Don’t Measure Once” makes the same point: treat visibility as a distribution and sample it repeatedly. For the wider framework of what to track, see our guide to measuring AI visibility.

The core GEO metrics

• Mention rate: the share of answers that name your brand.
• Citation rate: the share of answers that link to your domain.
• Position: where you appear among the brands named, useful only as an average across many runs.
• Sentiment: whether the description is positive, neutral or negative.
• Share of voice: your mentions as a share of all mentions for the brands you track.

A worked example with numbers

Here is a hypothetical setup to show how the arithmetic works; the numbers are illustrative, not from a real brand. A project-management SaaS tracks 40 buyer prompts on 3 engines and runs each prompt 5 times a week. That is 40 × 3 × 5 = 600 answers per week.

• The brand is named in 138 answers: mention rate = 138 ÷ 600 = 23%.
• Its domain is cited in 54 answers: citation rate = 54 ÷ 600 = 9%.
• Across all 600 answers, the five tracked competitors plus the brand collect 920 mentions: share of voice = 138 ÷ 920 = 15%.

The gap between 23% mentions and 9% citations is itself a diagnosis. The engines know the brand, probably from third-party sources, but rarely use its own pages as evidence. That points to on-page citability work rather than more PR. If the gap were reversed, with many citations but few recommendations, the brand would need stronger off-site reputation.

Repeated runs matter because a single run swings widely. With 5 runs per prompt you can tell a real 5-point move from noise far more reliably than with one manual check. Tools such as Asheard’s AI visibility tracker automate this kind of sampling across ChatGPT, Gemini, Claude, Perplexity, Grok, Google AI Overviews, Google AI Mode, Microsoft Copilot, DeepSeek and Mistral, with per-prompt location settings and competitor share of voice.

First-party signals to add

Combine prompt tracking with what your own data shows. As of October 2026, Google Search Console’s generative AI reports show impressions for AI Overviews and AI Mode but no clicks. Bing Webmaster Tools reports Copilot citations. GA4 now groups many assistant referrals into an “AI Assistant” channel, although AI Overviews and AI Mode clicks still sit in Organic Search. None of these alone gives the full picture, which is why prompt-level tracking remains the backbone.

A starter GEO plan for the first 30 days

You do not need a large team to begin. This sequence works for a single marketer and puts measurement first, so every later change can be judged against a baseline.

1. Days 1 to 3: Pick your prompts. Write 30 to 50 prompts real buyers would ask, split across category questions (“best X for Y”), comparisons (“X vs Y”) and problem questions. Include your main competitors as tracked brands.
2. Days 4 to 10: Record a baseline. Run every prompt several times on the engines your audience uses. Log mention rate, citation rate, share of voice and any factual errors about your brand.
3. Days 8 to 12: Fix access. Check robots.txt for blocked search bots, confirm key pages render their main content in raw HTML, and make sure they are indexed in Google and Bing.
4. Days 10 to 20: Rewrite five pages for citability. Choose the pages that should answer your top prompts. Add a direct answer under each heading, real numbers with sources, and comparison tables where buyers compare options.
5. Days 15 to 30: Close the off-site gap. Look at which domains engines cite for your prompts. Get your facts corrected on review sites and directories, and pursue mentions on the sources that appear most often.
6. Day 30: Re-measure. Compare against the baseline per prompt and per engine, not just in aggregate. Keep what moved the numbers and drop what did not.

Expect slow movement on engines that answer mostly from training data and faster movement on engines that search live for every prompt. That difference is the main reason to track engines separately rather than as one blended score.

Common GEO mistakes

• Judging by one screenshot. A single answer is a sample of one. Decide only on repeated runs.
• Keyword stuffing for AI. The original GEO paper found it scored below the unedited baseline.
• Blocking the wrong bot. Disallowing a training crawler is a policy choice; disallowing a search crawler removes you from answers.
• Ignoring what others say about you. Your own site is one source among many, and often not the most trusted one.
• Chasing every fan-out variant with a new page. Google warns that mass-producing near-duplicate pages can breach its scaled content abuse policy. One strong page that covers the subtopics usually wins.

Frequently asked questions

What is generative engine optimization in simple words?

It is the work of getting your brand named and your pages cited when AI tools such as ChatGPT, Gemini, Perplexity or Google AI Overviews write an answer. Instead of ranking a link in a list, you aim to be part of the answer itself, described accurately and recommended for the questions your buyers actually ask.

Who coined the term generative engine optimization?

The term comes from the paper GEO: Generative Engine Optimization by Pranjal Aggarwal, Vishvak Murahari, Tanmay Rajpurohit, Ashwin Kalyan, Karthik Narasimhan and Ameet Deshpande. It was posted to arXiv in November 2023 and published at KDD 2024. The paper introduced GEO-bench and showed that some content edits raised source visibility in AI answers by up to 40%.

Is GEO different from SEO?

GEO builds on SEO but targets a different outcome. SEO aims for ranked links and clicks, while GEO aims for mentions, citations and accurate descriptions inside AI answers. Many foundations are shared, such as crawlability, indexing and quality content, but GEO adds off-site reputation work and new metrics like mention rate and share of voice across AI engines.

How long does GEO take to show results?

It depends on the engine. Engines that search live, such as Perplexity or Google AI Mode, can reflect page and citation changes within weeks once pages are recrawled. Answers drawn from training data change only when models are updated, which can take months. Measure each engine separately against a baseline so you can see which changes actually moved.

Can you track GEO results in Google Analytics?

Partly. GA4 groups many assistant referrals, such as ChatGPT and Gemini, into an AI Assistant channel, but clicks from Google AI Overviews and AI Mode stay in Organic Search, and many AI visits arrive without a referrer. Analytics shows traffic, not whether you were mentioned, so pair it with repeated prompt tracking across engines.