What is generative engine optimization (GEO)?

Generative engine optimization (GEO) is the practice of making your website, brand and products more likely to appear in answers written by AI systems such as ChatGPT, Google AI Overviews, Google AI Mode, Gemini, Perplexity and Claude. In classic search the goal is a high-ranking blue link. In GEO the goal is to be named, recommended or cited inside the answer itself.

You will see several labels for roughly the same work. AEO (answer engine optimization), LLMO (large language model optimization) and AI SEO all describe optimizing for AI-written answers. The differences are mostly emphasis, which we cover in GEO vs SEO vs AEO. Google's position is blunt: from its point of view, optimizing for generative AI search is still SEO.^[3]

A practical definition that works for a software company in Toronto, a clinic in Melbourne or an online store in São Paulo: GEO is everything you do so that, when someone asks an AI a question your business can answer, the engine can find your pages, understands what you offer, trusts the sources that mention you, and has a reason to name you.

The term was coined in a research paper titled "GEO: Generative Engine Optimization" by Pranjal Aggarwal, Vishvak Murahari, Tanmay Rajpurohit, Ashwin Kalyan, Karthik Narasimhan and Ameet Deshpande. It was first posted to arXiv on 16 November 2023 and published at KDD 2024.^[1]

The authors built a benchmark called GEO-bench of 10,000 queries drawn from nine datasets, then tested nine ways of editing a source page to see which ones changed how much of that page a generative engine used in its answer. Their main test engine was GPT-3.5-turbo working from the top five Google results, and they repeated key tests on Perplexity.^[1]

Two findings are worth remembering. First, effects varied a lot by subject area, so there is no universal recipe. Second, pages that ranked lower in the underlying search results gained much more than top-ranked pages.^[1] Keep the limits in mind too: this was a controlled lab setup with an older model, not a test of today's production engines, which retrieve and rank sources in their own ways.

Most answer engines search first and write second. They run one or more searches, read a handful of retrieved pages, and write an answer grounded in them, often with links. Google describes this as retrieval-augmented generation using its Search index, and says AI Mode and AI Overviews may use query fan-out, issuing many related searches across subtopics to gather a wider set of sources.^[3][2]

That has a simple consequence: if an engine cannot retrieve your page, it cannot cite it. Google says a page must be indexed and eligible to show a snippet to be a supporting link, with no extra technical requirements.^[2] Other engines use their own crawlers, and each one can be blocked separately in robots.txt.

Training crawlers are a separate choice. OpenAI states that its training crawler (GPTBot) and its search crawler are controlled independently, so you can opt out of training without leaving ChatGPT search, and that robots.txt changes take about 24 hours to register.^[4] Our guide to AI crawlers and robots.txt walks through each bot.

After retrieval, the model decides what to say. Nobody outside the engine companies can see that step, and Google warns that no third-party tool has access to its internal ranking or AI systems.^[3] What you can observe is the output: which brands get named, which pages get cited, and how that changes over time.

The tactics that hold up are the ones that make you easier to retrieve and more credible to cite. None is new, but each matters more when a model is summarizing the web for someone.

1. Let the search crawlers in. Check robots.txt, your firewall and your CDN for OAI-SearchBot, PerplexityBot, Claude-SearchBot and Googlebot. Cloudflare began blocking AI crawlers by default in July 2025, so a site can block them without anyone deciding to.^[11] Run the free AI crawler checker to see what each bot gets.
2. Answer the real question on the page. Put a direct, self-contained answer near the top of each key page: what you sell, who it is for, where you operate, what it costs or how pricing works. Engines quote passages, so make the passage worth quoting.
3. Add evidence you can stand behind. The GEO paper found that statistics, quotations and cited sources helped most.^[1] Use your own numbers (with dates), named experts and links to primary sources. Never invent figures.
4. Publish things only you can say. Google advises unique, first-hand content over recycled summaries of common knowledge.^[3] Original data, real comparisons and clear pricing pages are hard for an engine to find elsewhere.
5. Be described consistently elsewhere. Engines read reviews, directories, forums, press and comparison pages. Make sure your category, location and product names match across them. Find which sites engines actually cite with Sources and fixes.
6. Keep business and product data clean. For local businesses and stores, Google points to Business Profile and Merchant Center data as inputs to both AI responses and regular results.^[3]

Plenty of GEO advice is untested or contradicted by the engines themselves. Be skeptical of anything sold as a shortcut.

• Keyword stuffing. It did little or nothing in the GEO study.^[1]
• llms.txt as a Google ranking lever. Google says you don't need AI text files or special markup for its generative AI features.^[3] An llms.txt file can still help agents read your docs; see llms.txt explained, but don't expect it to move AI Overviews.
• Chopping pages into tiny chunks or writing a separate version for AI. Google says neither is required.^[3]
• Manufactured mentions. Google lists chasing inauthentic mentions across the web as unhelpful.^[3] Fake reviews and paid placements disguised as editorial also carry legal and reputational risk.
• Guarantees. No vendor or agency can promise that an engine will recommend you. Answers change between runs and between model updates.

Measure the share of answers that mention you, across many questions and repeated runs, and always look at how many answers sit behind the number. The SparkToro study concluded that rank position in AI answers is unreliable, while visibility percentage across many runs is a reasonable way to compare brands.^[8]

1. Pick questions buyers actually ask, without your brand name in them. Branded questions ("is X any good?") almost always mention you, so keep them separate.
2. Ask each topic several ways and repeat over weeks. Pool the answers into a rate, such as "named in 34 of 120 answers".
3. Show a range, not just a point. With small samples, a move of a few points is usually noise. Our methodology and Honest Score explain how we report a 90% range and flag changes that are within noise.
4. Measure per market and language. A rate measured in the US says little about Germany or Japan.
5. Add first-party data. Google introduced Search generative AI performance reports in Search Console in June 2026.^[9] Bing Webmaster Tools added an AI Performance report covering citations in Copilot and Bing's AI summaries in February 2026.^[10] ChatGPT adds utm_source=chatgpt.com to referral links, which you can segment in analytics.^[5] Our GA4 guide shows the setup.

If you are starting from zero, this order gets the most certain wins done first and gives you a baseline before you change anything.

1. Week 1: baseline. Write 20 to 40 unbranded buyer questions per market. Measure how often each engine names you and which competitors it names instead.
2. Week 1: access. Fix any crawler blocks in robots.txt, firewall rules or CDN settings.
3. Week 2: key pages. Rewrite the top of your home, product, pricing and location pages so each answers its main question directly, with dated facts.
4. Week 3: sources. List the third-party sites engines cite for your questions. Correct wrong listings, fill missing ones, and pitch genuine reviews or comparisons where you belong.
5. Week 4 and after: re-measure. Compare rates with ranges. Keep changes that move the rate beyond noise; stop work that doesn't.