What is generative engine optimization (GEO)? A practical guide

What is generative engine optimization?

Generative engine optimization is the work of improving how often, and how accurately, AI systems mention, cite and recommend your brand. A generative engine is any system that writes an answer instead of returning a list of ten links: ChatGPT, Gemini, Perplexity, Claude, Microsoft Copilot, and Google's own AI Overviews and AI Mode.

The term comes from a 2023 academic paper by researchers at Princeton and several other institutions, which framed GEO as the counterpart to SEO for answer engines. It has since become the umbrella label that agencies and tool vendors use. You will also see answer engine optimization (AEO), LLM optimization (LLMO), AI SEO and AI search optimization. They describe the same territory; the differences are mostly branding.

GEO aims at three outcomes, and it helps to keep them apart because they are earned in different ways:

• Mention: the answer names your brand, with or without a link.
• Citation: the answer links one of your pages as a source for something it says.
• Recommendation: the answer puts you on a shortlist or suggests you for a specific use case. For SaaS, this is the outcome that maps most directly to pipeline.

These don't always travel together. Your docs page can be cited for a technical fact while a competitor gets recommended in the same answer. A well-known brand can be recommended from what the model learned in training, with no link at all. Our post on what AI brand mentions SEO is goes deeper on the mention side; this guide covers GEO as a whole.

Why generative engine optimization matters for SaaS

B2B buyers now ask AI tools the same questions they used to type into Google, and the answer they get back is a short list of named products with reasons attached. There is no position six to scroll down to. If you are not in that list, the buyer may never visit a search results page where you could have won the click.

The prompts that matter most for SaaS revenue are exactly the ones AI tools answer with opinions: "best tool for X", "alternatives to Y", "X vs. Y", "is X worth it for a small team". A traditional search result shows ten pages and lets the buyer decide. An AI answer has already done some of the deciding.

The second reason is control over your own story. An AI answer about your product is assembled from what other people wrote: review profiles, comparison posts, forum threads, old press coverage, your docs. If those sources are stale or wrong, the model repeats them with confidence. A good part of GEO is making sure the facts in circulation about you are current and consistent.

GEO also compounds with SEO rather than replacing it, since crawlable pages, authority and useful content serve both. We made the wider case in AI answer engines are the new search results page; the rest of this guide is about the mechanics.

How generative engines build an answer

No AI company publishes its full selection logic, but the broad architecture is public, and it explains most of what GEO work does.

What the model already knows

Large language models are trained on big snapshots of web text and other data. What a model says about your brand without searching comes from there: how often your name appeared, next to which topics, on which kinds of sites. You can't edit that memory directly, and it only changes when a new model is trained, which lags the real world. Brands that were widely discussed before the training cutoff tend to be recalled; newer or niche brands often aren't, or get described with old facts.

What the model looks up

For questions that need current or specific information, most assistants now search the web, read what comes back, and ground the answer in those pages. This is called retrieval-augmented generation, and it is where citations come from. OpenAI says ChatGPT search uses third-party search providers alongside its own crawler, OAI-SearchBot. Google's AI Overviews and AI Mode draw on Google's own search index. Perplexity searches the web for almost every question and shows its sources by default.

Query fan-out

Google has said AI Overviews and AI Mode may use a technique it calls query fan-out: running several related searches across subtopics to build one response. OpenAI similarly describes ChatGPT rewriting a prompt into one or more targeted search queries. The practical effect is that your page doesn't need to rank for the exact prompt. It needs to be a strong result for one of the sub-questions. A prompt like "best API monitoring tool for a small team" might fan out into searches about pricing, alerting, integrations and reviews, and each of those is a separate chance to be retrieved.

Selection and synthesis

Once candidate pages are retrieved, the model reads them and writes the answer, attaching citations to the claims each source supports. This step is the least documented. What is reasonable to infer, and what we see in practice, is that pages are easier to use when they state the needed fact plainly, match the sub-question closely, come from sources the search layer already trusts, and agree with what other sources say. We break this down for ChatGPT in how ChatGPT chooses which sources to cite and for Google in how to rank in AI Overviews.

GEO vs. SEO: what carries over and what is new

GEO is not a separate discipline that starts from zero. It is SEO with a different target and a wider playing field. The table shows where the two line up and where they split.

The last row is the one people skip. If Google can't index a page, AI Overviews can't cite it. If a page doesn't show up in the search layer an assistant uses, it is unlikely to be retrieved at all. Weak SEO makes GEO harder, and GEO is not a shortcut around it.

The five pillars of a generative engine optimization strategy

Most GEO work falls into five areas. They build on each other in roughly this order, so a gap early in the list limits everything after it.

Access: let AI search crawlers reach your pages

Your robots.txt file decides which crawlers can fetch your pages, and the AI crawlers are not interchangeable. OAI-SearchBot is what lets your pages appear as sources in ChatGPT search; GPTBot is OpenAI's separate training crawler. Google-Extended controls whether Google can use your content for Gemini training and grounding, but it does not affect Search or AI Overviews, which run on Googlebot. PerplexityBot is Perplexity's crawler. Plenty of sites blocked every AI bot in one rule and removed themselves from AI search answers by accident.

Check what your file actually allows with our robots.txt tester, then check your CDN or firewall, since some offer one-click AI bot blocking that overrides robots.txt entirely. Finally, make sure key content is in the HTML the server sends. Googlebot renders JavaScript, but we would not assume every AI crawler does, so a React or single-page marketing site should prerender or server-render the pages you want cited.

Extractability: write pages a model can quote

A model building an answer looks for a passage that settles the sub-question it is working on. Pages that give it one are easier to cite. That means the answer near the top of each section, one idea per section, headings that mirror the questions buyers ask, comparisons in tables, and facts written as sentences rather than hidden in images, tabs or PDFs behind a form.

Specific beats impressive. "Supports SAML SSO on the Business plan" can be quoted; "enterprise-grade security" can't. The same goes for pricing, rate limits, integrations and supported languages: if a buyer would ask about it, state it plainly somewhere crawlable.

Corroboration: get described by sources you don't own

Your own site calling you the best option is a claim. A review profile, an independent comparison article, a community thread and a trade publication saying similar things is corroboration, and AI answers lean heavily on it, especially for recommendation prompts. This is the brand mentions side of GEO: getting your product placed, accurately, in the third-party content that answers in your category are built from.

Backlinks still matter here, because the authority they build helps your pages rank in the search layer that retrieval depends on. Links and mentions do different jobs and work best together.

Entity consistency: one story about who you are

Models and search engines treat your brand as an entity: a thing with a name, a category, a description and relationships to other things. They piece that picture together from every profile they can find. If your homepage says "observability platform", your G2 profile says "log management tool" and an old directory listing still shows pricing from two years ago, the model either hedges or repeats the stale version.

Use the same name, one-line description, category and current facts across your site, review profiles, LinkedIn, Crunchbase, GitHub, partner directories and any reference pages you legitimately qualify for. Organization schema with sameAs links to those profiles helps machines connect them; our schema markup generator builds that block for you.

Measurement: track prompts, not just keywords

Rank trackers don't tell you whether ChatGPT recommends you. GEO measurement starts with a fixed set of prompts your buyers would ask, run across the platforms that matter, several times each because answers vary between runs. For each run you record whether you were mentioned, cited or recommended, which competitors appeared, which URLs were cited, and whether what the answer said about you was accurate.

Alongside that, AI referral traffic shows up in analytics from referrers such as chatgpt.com, perplexity.ai and gemini.google.com. Google Search Console counts AI Overviews and AI Mode traffic inside its Web search totals rather than reporting it separately, so it can't isolate that slice for you.

How to start with generative engine optimization

This is the order we would work in for a SaaS company starting from nothing. Steps 1 to 5 are mostly one-off; steps 6 to 8 are the ongoing work.

1. Write down a few dozen prompts your buyers actually ask, grouped by stage: category discovery ("best tool for X"), comparison ("X vs. Y"), alternatives, problem-led how-to questions, and questions about your brand by name.
2. Run each prompt in ChatGPT, Gemini, Perplexity and Google (AI Overviews and AI Mode) more than once, and log the results in a spreadsheet: mentioned, cited, recommended, which competitors, which URLs.
3. Sort every cited URL by type: your site, competitor sites, review platforms, publications, forums, documentation. This tells you where the answers in your category are actually formed.
4. Fix access before anything else: robots.txt rules for AI search crawlers, CDN bot settings, accidental noindex tags, and content that only renders in the browser.
5. Fix the facts: one consistent description, category and pricing across your site and every profile you control, and requests to update the listings you don't.
6. Build or rewrite the owned pages that should be cited: comparison and alternatives pages, use-case pages, a pricing page with real numbers, and docs that answer integration questions directly.
7. Earn coverage on the third-party sources from step 3: accurate inclusion in comparison articles, complete review profiles, genuine answers in communities, and articles on publications your buyers read.
8. Re-run the same prompt set every month and compare trends across many runs, not single answers.

GEO myths worth ignoring

These are the claims we see most often that don't hold up against what the platforms have actually said.

• "You need an llms.txt file." llms.txt is a proposed convention, and no major search engine or AI company has confirmed using it to choose sources. It is cheap to add if you want one, and our llms.txt generator builds it from your sitemap, but don't expect it to move anything on its own.
• "Schema markup gets you into AI answers." Google says there is no special markup required to appear in AI Overviews. Structured data helps machines understand a page and connect your entity profiles; it is not a switch.
• "Blocking Google-Extended keeps you out of AI Overviews." It doesn't. AI Overviews are part of Google Search and follow Googlebot and the normal snippet controls.
• "GEO replaces SEO." Retrieval runs on search indexes. A site that can't rank has less to be retrieved.
• "Write for the bots." Hidden text, keyword-stuffed prompt lists and instructions aimed at AI crawlers are the same tricks search spam policies already cover, and they make pages worse for the buyers who read them.
• "We can guarantee AI citations." Nobody controls a model's output. Anyone promising a fixed number of citations or a guaranteed recommendation is selling something they can't deliver.

How Chung Digital approaches generative engine optimization

Our Mentions service is built around the pillars above, with the heaviest weight on corroboration, because that is the part most SaaS teams can't do in-house at volume. As a generative engine optimization agency, we start every engagement with a baseline audit of your AI visibility across ChatGPT, Gemini, Perplexity and AI Overviews, then track a fixed set of target prompts every month so progress is measured against the same questions.

• Starter ($699/mo): baseline audit, tracking for 15 to 20 target prompts, 2 to 3 brand mention placements a month, and a monthly visibility report.
• Growth ($1,599/mo): 40 to 50 prompts, 5 to 6 placements a month, entity and schema optimization on your site, and a competitor citation gap analysis.
• Authority ($3,199/mo): 100+ prompts across all major AI platforms, 10+ placements a month, full schema and content optimization for AI parsing, and a quarterly competitive benchmark report.

Plans are month to month, with no long-term contract. If the audit shows the real blocker is technical, crawl access, rendering or indexing, we will say so, and that work sits better with our SaaS SEO services. What we won't do is promise a citation count. We can improve the odds and show you, prompt by prompt, whether it is working.