2026-09-16, GEO · Fundamentals

What is generative engine optimization (GEO)?

Generative engine optimization (GEO) is the practice of increasing how often, and how accurately, AI assistants mention, cite and recommend your brand inside the answers they generate. The engines in scope today are ChatGPT, Perplexity, Gemini, Copilot, Grok, and Google’s AI Overviews and AI Mode.

SEO measures whether a page ranks. GEO measures whether the answer names you, which of your pages the engine used, and which competitors it recommended instead.

Where the term comes from

The name was coined in the research paper GEO: Generative Engine Optimization (Aggarwal, Murahari, Rajpurohit, Kalyan, Narasimhan, Deshpande), posted to arXiv in November 2023 and accepted at KDD 2024 (checked 2026-09-17). The paper frames generative engines as systems that use large language models to gather and summarize information from several sources into one response, and treats content visibility inside those responses as a black-box optimization problem.

Three findings from the paper, with their scope:

• The authors built GEO-bench, a benchmark of 10,000 queries split 8K/1K/1K into train, validation and test sets.
• On that benchmark, their best methods improved source visibility by up to 40%. The strongest were Quotation Addition, Statistics Addition and Cite Sources; Keyword Stuffing scored below the unoptimized baseline on their position-adjusted word count metric.
• Effectiveness varied by domain, so no single tactic won everywhere. They also ran the methods against Perplexity.ai.

Those results come from a controlled setup with specific metrics (a position-adjusted word count and an LLM-judged “subjective impression” score), per the paper’s full text (checked 2026-09-17). They show that content changes can move visibility; they do not predict a number for your site. Measure that on your own prompts.

Ranking work stops at the middle of this chain; GEO is judged one stage further along, inside the answer the engine writes.

Why GEO is a separate discipline

A search results page is a ranked list. An AI answer is composed: the engine searches, reads several pages, picks a few to cite, and writes one response. That changes what you can observe and optimize.

Three outcomes instead of positions

There is no “rank 4” inside a paragraph. For any prompt, your brand is in one of these states:

Mentions and citations are independent: an answer can recommend you while citing a review site, or cite your docs without naming you. Mentions vs citations covers why you track both.

The answer depends on who asks, where and when

The same prompt returns different answers in different countries, in different US states, and from one run to the next. GEO measurement therefore has to be geo-targeted and repeated. A single manual check in your own browser is one sample, from one location, with your account history attached. See AI answer volatility for how to handle the run-to-run variance.

The engine searched before it wrote

Answers that cite web pages were built from searches the engine ran on the user’s behalf, often several of them, a pattern called query fan-out. Google documents this for its own features: its AI features page says AI Overviews and AI Mode may issue multiple related searches across subtopics and data sources to build a response (checked 2026-09-17). The API returns Grok’s web and X searches in searchQueries[]. ChatGPT has the same field (with include.searchQueries, which is free), but it comes back empty on the answers served today.

So GEO includes being findable for the fan-out queries, not only for the user’s literal prompt. Query fan-out explained goes deeper.

GEO vs SEO at a glance

The technical baseline carries over directly. Google states that there are no additional requirements to appear in AI Overviews or AI Mode, that no special schema.org markup or AI text files are needed, and that a page must be indexed and snippet-eligible to be shown as a supporting link (AI features and your website, checked 2026-09-17). For ChatGPT, OpenAI’s crawler documentation says sites that opt out of OAI-SearchBot are not shown in ChatGPT search answers, while GPTBot governs training use and does not affect search results (checked 2026-09-17). Check that your robots.txt is not blocking the crawler you want to be cited by.

For the metric-by-metric mapping, see GEO vs SEO metrics; for moving an existing program over, see extending an SEO program to AI answers.

GEO work, step by step

1. Build a prompt set. Collect the questions buyers ask at each stage: category (“best project management tool for agencies”), comparison (“Asana vs Monday for a 20-person team”), and problem (“how do I stop missing client deadlines”). Group them by intent. Designing a prompt set covers size and wording.
2. Pick engines and markets. Choose the engines your buyers use and the countries you sell in. Each request carries a country; ChatGPT, Gemini, AI Mode and Google Search answer from 213 countries, and Copilot, Perplexity and Grok take any country. Gemini also takes a US state; the other assistants answer at country level.
3. Measure on a schedule. Run the full set repeatedly, at least weekly, and store every raw response. Trends over weeks are signal; single runs are noise. Monitoring cadence helps choose the interval.
4. Analyze sources. For prompts where you are absent, list the domains that are cited. They are often review sites, community threads and comparison articles rather than brand homepages. Those pages are where you need presence: your own comparable page, a listing, or coverage.
5. Improve citability. Publish pages that state specific, attributable claims (numbers, specs, prices, dated facts) with clear headings that match the question. That is consistent with the paper’s finding that adding quotations, statistics and citations helped, and that keyword stuffing did not.
6. Re-measure and attribute. Compare mention rate and citation share before and after a change, on the same prompts and markets, and against a control set you did not touch.

Measuring it with an API

A monitoring pipeline sends each prompt to each engine and stores structured JSON. One ChatGPT check in Python with requests, computing whether the answer mentions and cites a brand:

import os
from urllib.parse import urlsplit

import requests

API = "https://api.answerline.dev"
HEADERS = {"Authorization": f"Bearer {os.environ['ANSWERLINE_API_KEY']}"}

BRAND = "Acme"
DOMAIN = "acme.com"

resp = requests.post(
    f"{API}/v1/monitor/chatgpt",
    headers=HEADERS,
    json={
        "prompt": "What is the best invoicing software for freelancers?",
        "country": "US",
    },
    timeout=360,
)
resp.raise_for_status()
result = resp.json()["result"]

mentioned = BRAND.lower() in result.get("text", "").lower()
cited = [
    s["url"]
    for s in result.get("sources", [])
    if (urlsplit(s["url"]).hostname or "").removeprefix("www.") in (DOMAIN,)
    or (urlsplit(s["url"]).hostname or "").endswith("." + DOMAIN)
]

print("mentioned:", mentioned)
print("cited pages:", cited)
print("charged (USD):", resp.headers.get("X-Credits-Charged"))

That call costs $0.0006 at the Starter tier. For a recurring prompt set, submit tasks in batches of up to 500 and collect results with webhooks.

Across a prompt set, two numbers carry most of the signal:

mention_rate   = answers that name you / answers collected
citation_share = sources[] entries on your domains / all sources[] entries

Compute both per engine and per market. A number blended across engines hides differences, because each engine searches and cites differently. The AI share of voice framework adds coverage and answer-position metrics, and measuring brand mentions in ChatGPT handles aliases and false positives.

Example budget

A starting program of 25 prompts, run weekly on ChatGPT ($0.0006) and Gemini ($0.0006), costs $0.0012 per prompt per run, $0.03 per run, and $0.12 over four weekly runs. New accounts get 100 free requests on any live engine, which cover 2 of those weekly runs; pricing lists the tier prices for the rest, and cost planning walks through larger programs.

Common mistakes

• Treating one answer as the truth. Answers vary per run. Report rates over repeated samples, not a screenshot.
• Blending engines. ChatGPT and Gemini cite different sources for the same question. Keep them in separate columns.
• Tracking only branded prompts. “What is Acme?” tells you little. Category and comparison prompts are where recommendations happen.
• Counting mentions by naive substring. Short or common brand names produce false positives; use aliases and word boundaries.
• Chasing AI-specific files or markup. Google says none is required for its AI features. Spend the effort on content the engines can cite and on the pages they already cite.
• Ignoring markets. A brand visible in US answers can be absent in Germany. See multilingual AI answers and state-level answers.

GEO and SEO together

Classic search still matters: people use it, and many AI answers are assembled from searches the engine runs. Measuring both on the same prompt set shows where they diverge.

To start collecting answers, follow the quickstart or see the AI visibility tracking use case.

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