Generative engine optimization is the process I use as a GEO consultant to increase the probability that a brand, website, or piece of content gets discovered, understood, referenced, and cited by AI-powered search and answer systems.

The important word there is probability.

I cannot promise that following a GEO checklist will make ChatGPT cite your website next Tuesday. Nobody can. Generative systems are variable. Answers can change based on the model, prompt, retrieval system, location, personalization, index freshness, and other factors we cannot see.

What we can do is improve the inputs. The original academic work on this, the 2023 paper that named the discipline, framed it the same way: as measurable optimization of visibility inside generated responses, not a guarantee of placement.

That means making a website technically accessible, building the topical and entity signals that search systems can understand, publishing information worth retrieving, and measuring whether those changes correspond with greater visibility.

I have been working in SEO and digital marketing since 2013. My GEO framework is an extension of that work, not a replacement for it. Google itself says its existing SEO best practices remain relevant to AI Overviews and AI Mode, and its current documentation describes retrieval-augmented generation as one of the techniques connecting generative AI features with Google’s core Search systems.

So if somebody tells you SEO is obsolete and you need to throw everything away for a shiny new GEO playbook, I would keep one hand on your wallet.

Here is the 12-step process I run instead.

1. Establish Your GEO Baseline

Action: Build a benchmark of how your brand currently appears across major generative engines.

Before changing the website, I want a baseline.

I create a set of prompts representing actual user intent across the customer journey. For a B2B SaaS company, those might include category questions, alternatives, comparisons, use cases, implementation questions, and buying criteria.

Then I manually test those prompts across relevant systems such as ChatGPT, Perplexity, Google Gemini, AI Overviews, Claude, and Copilot.

I record:

  • Whether the brand appears

  • Whether the website earns citations

  • Which competitors appear

  • Which third-party sources get cited

  • How the engine describes the brand

  • Whether the answer is accurate

  • Which URLs are referenced

This becomes my GEO benchmark.

Do not obsess over a single prompt. Large language models can produce different answers to identical or similar questions. I care more about patterns across a controlled prompt set.

2. Protect the SEO Foundation

Action: Verify that important pages can be crawled, indexed, rendered, and internally discovered.

GEO does not rescue bad technical SEO.

Google explicitly says a page must be indexed and eligible to appear in Google Search with a snippet to qualify as a supporting link in its generative AI features. Google also recommends familiar SEO practices such as crawl accessibility, internal linking, textual content, page experience, and accurate structured data.

I therefore audit:

  • robots.txt

  • XML sitemaps

  • canonical tags

  • indexation

  • internal links

  • HTTP status codes

  • JavaScript rendering

  • page speed and usability

  • metadata

  • duplicate or conflicting pages

Think of traditional SEO as the foundation and GEO as an additional optimization layer. You can call the broader discipline search engine optimization, answer engine optimization, AEO, LLMO, or AI search optimization. The terminology matters less to me than whether the underlying system is technically sound.

If search engines cannot reliably discover the information, I am not going to bet on generative engines magically fixing the problem.

3. Audit Access for AI Crawlers

Action: Check robots.txt, CDN, WAF, and server configurations for the AI crawlers you intentionally want to permit.

This is where GEO starts getting more platform-specific.

For example, OpenAI says publishers that want content included in summaries and snippets in ChatGPT search should make sure OAI-SearchBot is not blocked. OpenAI also distinguishes OAI-SearchBot from GPTBot, which publishers can block when they want to opt content out of potential model training.

That distinction matters.

I review access for relevant AI crawlers and then check the rest of the infrastructure. A permissive robots.txt file does not help much if Cloudflare, a WAF, bot mitigation, authentication, or another security layer returns a 403.

OpenAI specifically recommends checking these additional layers when troubleshooting crawler access.

The goal is not “allow every bot.” The goal is to make an intentional decision about which systems should access which content.

4. Map Prompts to Topics and User Intent

Action: Turn your baseline prompt set into a topic-to-URL map.

Traditional keyword research is still useful, but GEO requires me to think beyond exact query strings.

A person might search Google for “best contract management software” and ask ChatGPT, “What contract management platforms are best for a 100-person SaaS company that needs Salesforce integration?”

Same underlying problem. Different interface.

I cluster prompts by:

  1. Problem

  2. Intent

  3. Entity

  4. Comparison set

  5. Required evidence

  6. Existing target URL

Then I identify gaps.

If 30 prompts about one buying problem all point toward a single generic service page, I probably have an information architecture problem.

This is where AEO and traditional SEO start overlapping heavily. Both reward content that resolves user intent clearly. GEO simply forces me to consider how that information may be retrieved and synthesized into an answer rather than displayed as ten blue links.

5. Build Pages Around Retrievable Answers

Action: Rewrite each important page so major questions have concise, self-contained answers.

Generative systems often need passages, not marketing fog.

I structure important sections around a simple pattern:

Question → direct answer → evidence → explanation → next logical question.

If a user asks what something costs, I want the relevant page to explain pricing as directly as the business permits. If the question is how two products differ, I want an explicit comparison rather than expecting a system to infer the answer from six paragraphs of positioning copy.

This is a practical GEO hypothesis based on how retrieval systems operate, not a universal platform guarantee.

Google has documented that its AI search features can use a “query fan-out” approach to issue multiple searches across related subtopics and data sources. It also recommends making important information available in textual form.

That makes comprehensive topic coverage useful without giving us an excuse to write 6,000 words of oatmeal.

6. Add Original Evidence and First-Hand Expertise

Action: Add at least one source of proprietary or first-hand information to every strategic page.

If 50 articles say essentially the same thing, why should an AI system retrieve yours?

This is where I push clients toward information competitors cannot reproduce without doing the work:

  • Original data

  • Experiments

  • Benchmarks

  • Customer outcomes

  • Screenshots

  • Expert commentary

  • Proprietary frameworks

  • Methodology

  • First-hand observations

  • Specific examples

This supports traditional E-E-A-T thinking while also making the page more differentiated.

Google’s guidance for AI search specifically emphasizes unique, valuable, people-first content rather than commodity information.

My tested hypothesis is that unique evidence also creates better citation opportunities because it gives a generative system something specific to attribute to you.

“Content marketing is important” is disposable.

“We analyzed 412 SaaS landing pages and found X” gives the system a source worth citing.

7. Strengthen Entity and Brand Signals

Action: Make your brand, people, products, services, and relationships consistently identifiable across your site and relevant external sources.

GEO is not only about getting URLs cited. I also care about brand mentions.

A generative engine needs to understand what an entity is before it can confidently explain when that entity is relevant.

I check consistency around:

  • Brand naming

  • Product and service terminology

  • Founder and expert bios

  • Organization information

  • About pages

  • Third-party profiles

  • Industry directories

  • Reviews

  • Editorial mentions

  • Relevant communities

That can include platforms such as Quora when they genuinely contain conversations relevant to the market. I am not advocating mass account creation or dropping promotional answers everywhere. That is spam wearing a tiny GEO hat.

The objective is consistent entity information and legitimate brand visibility across sources people and machines may encounter.

8. Earn Third-Party Mentions, Links, and Citations

Action: Identify the external sources already appearing for your target prompts and build a digital PR plan around them.

Backlinks still matter, but I broaden the question for GEO:

Where does the market talk about this category?

During my baseline tests, I collect the domains that ChatGPT, Perplexity, Gemini, and other engines reference.

Then I categorize those sources:

  • Publishers

  • Industry organizations

  • Review platforms

  • Forums

  • Comparison sites

  • Research organizations

  • Expert websites

  • Communities

I look for legitimate opportunities to earn backlinks, editorial coverage, inclusion in research, expert quotes, reviews, and brand mentions.

I treat third-party corroboration as strategically important to GEO because generative answers may draw from multiple web sources and because independent references can reinforce what a brand says about itself. I do not treat a mention on any particular website as a guaranteed AI ranking factor.

The distinction between documented behavior and tested hypothesis matters.

9. Implement Useful Structured Data

Action: Add valid schema markup that accurately describes the visible content.

Structured data helps machines interpret explicit information about a page. Google says structured data provides clues about page meaning and can make content eligible for supported search features.

Depending on the site, I may implement appropriate Organization, Article, Product, LocalBusiness, Breadcrumb, ProfilePage, or other supported schema markup.

But I do not sprinkle JSON-LD on a website like parmesan and call it GEO.

Google specifically says there is no special schema.org structured data required to appear in AI Overviews or AI Mode.

One note about this article’s 12-step format: it is intentionally structured so the process can be represented using HowTo schema where appropriate outside Google. However, Google does not currently list HowTo among its supported structured-data search appearance features, so I would not promise a Google rich result from HowTo markup.

Schema should describe reality, not manufacture it.

10. Treat llms.txt as an Experiment, Not a Ranking Hack

Action: Decide whether to publish an llms.txt file, document the implementation, and test the result.

I include llms.txt in my GEO audits because clients ask about it constantly.

I do not treat it as mandatory.

Google explicitly says publishers do not need new machine-readable AI files to appear in AI Overviews or AI Mode.

I cannot verify that publishing llms.txt directly improves rankings or citations across major generative engines.

That does not mean you cannot test it. The implementation cost can be relatively small. Just put it in the experiment column rather than the “proven ranking factor” column.

The same discipline applies to plenty of LLMO and GEO tactics. Document the hypothesis, change one thing when possible, observe the outcome, and avoid turning correlation into folklore.

11. Optimize Titles, Metadata, and Supporting Content

Action: Align titles, headings, meta descriptions, internal anchors, images, and supporting pages with the topics you want the page to own.

I still optimize the boring stuff.

That includes:

  • Clear title tags

  • Descriptive headings

  • Useful meta descriptions

  • Internal links

  • Relevant anchor text

  • Descriptive image context

  • Supporting articles

  • Accurate author information

Why?

Because GEO does not happen in isolation from SEO. Google says its generative AI features are rooted in its Search ranking and quality systems.

This also helps humans understand the page, which remains a fairly underrated feature of websites.

Do not rewrite every heading into awkward AI bait. The goal is information clarity.

If a human cannot quickly determine what a section answers, I assume a machine may have unnecessary work to do too.

12. Measure GEO Visibility and Run Controlled Iterations

Action: Re-run your prompt set on a schedule and compare visibility, citations, referral traffic, and conversions against the baseline.

Measurement is where GEO becomes a marketing discipline instead of a collection of LinkedIn opinions.

I track metrics in four buckets:

Visibility: How frequently does the brand appear across the tracked prompt set?

Citation: Which pages earn citations, links, or source references?

Sentiment and accuracy: How do generative engines describe the brand, and is the information correct?

Business impact: Does AI referral traffic produce qualified visits, leads, pipeline, revenue, or other north-star outcomes?

OpenAI says publishers can track ChatGPT referral traffic in analytics and that ChatGPT search referral URLs include a utm_source parameter identifying chatgpt.com.

Google has also begun rolling out dedicated Search Console reporting for visibility in generative AI features such as AI Overviews and AI Mode to a subset of sites.

Then I iterate.

Maybe pages with original statistics gain more citations. Maybe comparison pages start appearing. Maybe brand mentions increase while referral traffic barely moves. Maybe nothing changes.

That last result is still data.

GEO measurement needs to acknowledge variance. I look for repeated changes across prompt groups and time periods rather than declaring victory because ChatGPT mentioned a client once on a Tuesday afternoon.

GEO Is an Extension of Good Search Strategy

My framework for generative engine optimization is deliberately less exciting than most GEO pitches.

Fix the technical foundation. Understand user intent. Make information retrievable. Publish original evidence. Strengthen entities. Earn third-party validation. Use structured data correctly. Test experimental protocols. Measure the results.

Then repeat.

The technology underneath generative search will continue changing. Google Gemini, ChatGPT, Perplexity, Claude, Copilot, and future systems will not all retrieve, rank, synthesize, or cite information identically.

That is precisely why I do not want a GEO strategy built around one trick. If your team cannot run these twelve steps in-house, that is usually where a GEO agency earns its keep, and I have written a full breakdown of how to choose one.

RAG, or retrieval-augmented generation, gives us one useful model for understanding why strong search fundamentals still matter: a large language model can produce a better grounded answer when a retrieval layer supplies relevant external information. Google now explicitly documents RAG as part of how its generative AI search experiences can connect to current web information.

The job is to make your organization a credible, accessible, useful source for that information.

SEO is not dead. AEO is not magic. GEO is not a cheat code.

It is another search surface to measure and optimize.

Frequently Asked Questions About Generative Engine Optimization

What metrics should you track for GEO?

I track generative visibility across a controlled prompt set, citations and source links, brand mentions, accuracy, AI referral traffic, conversions, and downstream business outcomes. Because generative answers vary, I prefer trends across multiple prompts and repeated tests rather than single-response rankings.

What is generative engine optimization used for?

Generative engine optimization is used to improve a brand’s likelihood of being discovered, understood, mentioned, or cited in AI-generated answers. GEO combines established SEO practices with content, entity, technical, authority, and measurement strategies designed around generative search experiences.

How does GEO differ from traditional SEO?

Traditional SEO has historically focused on visibility in conventional search results. GEO expands the scope to generative answers that may synthesize information from multiple sources. The disciplines overlap substantially because crawlability, relevance, authority, content quality, internal linking, and technical accessibility still matter.

What strategies are effective for generative engine optimization?

My GEO strategy starts with technical SEO, AI crawler access, prompt and intent research, retrievable answer formatting, original evidence, strong entity signals, third-party mentions, appropriate structured data, and recurring measurement. Some of these practices are supported by platform documentation. Others should be treated as hypotheses and tested against your own data.

How can generative engine optimization improve my website’s visibility?

GEO can improve the inputs that influence whether your content is available and useful to search and generative systems. That can create opportunities for your website or brand to appear as a source, citation, supporting link, or recommendation. It does not guarantee inclusion because generative engines ultimately control what they retrieve and display.

Is SEO dead now with AI?

No. Google explicitly states that SEO best practices remain relevant to its generative AI features, and those features draw on Google’s existing Search infrastructure and quality systems. AI changes how people search and how results are presented, but it does not eliminate the value of making information crawlable, relevant, authoritative, and useful.

Can I do GEO myself?

Yes. Start by building a representative prompt set, recording where your brand currently appears, auditing crawl and indexation, improving the answers on important pages, and measuring changes over time. More advanced GEO programs may require technical SEO, content strategy, digital PR, analytics, and experimentation, but you do not need a proprietary tool just to begin. If you are weighing outside help, here is how GEO pricing actually works.