The GEO Improvement Checklist: 6 Steps to Start Getting Cited by AI Engines

Run a quick test right now. Open Perplexity and type the question your ideal customer asks before they find you. Read the full response. Count how many times your brand gets mentioned.

If the answer is zero, that is your baseline. And that is exactly what this checklist fixes.

According to Semrush, ChatGPT hit 100 million users faster than any app in history, and Google‘s AI Overviews now reach billions of users each month. The shift is not gradual. Most websites are already invisible inside these AI-generated responses, and the gap between cited brands and invisible ones widens every quarter.

What follows is the exact checklist I use when auditing a site for AI visibility. Six steps, in order. Each one builds on the previous.

How to Audit Your Current AI Visibility Before You Touch a Single Page

A GEO audit tells you where you currently stand inside AI-generated responses before changing anything. Pick 10-15 questions your customers actually ask, run them through ChatGPT, Perplexity, Gemini, and Google AI Overviews, and record which brands and sources appear. That spreadsheet becomes your roadmap.

The queries need to be specific. Not “best project management software.” More like “best project management tool for a remote design team under $50 per month.” Specific queries surface real citation patterns. Generic queries surface whichever brand has dominated the web for a decade, which tells you very little about what GEO work can actually move.

Build the spreadsheet with four columns: query, engine, your brand mentioned (yes/no), competitor brands mentioned. After 15 queries across 4 engines, you have 60 data points. Patterns become obvious fast. You will likely find 2-3 competitors appearing consistently across engines. Those sites become your GEO benchmarks for the next 90 days.

Semrush’s AI Toolkit can automate the tracking side of this over time. Still worth doing the manual pass first. Reading actual AI responses forces you to notice things a dashboard misses: the specific framing AI uses, which sources get footnoted, what type of content gets extracted versus paraphrased. The data in a spreadsheet is useful. The intuition from reading 60 AI responses is irreplaceable.

The Fastest Way to Improve Your GEO? Rewrite These Pages First

Rewriting content to lead with direct answers is the single highest-leverage GEO change you can make. AI engines break queries into smaller chunks and match each chunk against the clearest available passage. Pages that bury the main point under three paragraphs of background get skipped. Pages that answer first get extracted and cited.

The Princeton GEO study found that adding quotable, self-contained statements improved AI citation rates by 27.8%. Answer-first structure is the prerequisite for this. You cannot write quotable statements if your content is built to build toward a conclusion. The answer has to come first, or nothing else in this checklist lands properly.

The rewrite formula, applied to every H2 section:

• First 1-2 sentences: Answer the question directly. No setup. No context. The answer.
• Next 2-3 sentences: Add the why, the nuance, or the exception that actually matters.
• Rest of the section: Evidence, examples, data with inline source attribution.

Apply this to every major content page targeting a query your customers search for. Yes, it is a lot of work. It also lifts your traditional featured snippet rate at the same time, so the investment covers two channels simultaneously.

Start with the 5-10 pages that already get the most organic traffic, or those targeting your highest-value queries. Not your homepage. The content pages where customers are trying to learn something specific.

Which Content Elements Do AI Engines Actually Extract?

Three types of content consistently get pulled into AI responses: attributed statistics, self-contained quotable statements, and clear inline definitions. Research from Princeton on GEO found that adding statistics with sources improved AI citation rates by 25.9%, while direct quotable passages improved them by 27.8%. Structure your content around these three patterns, in that order of effort.

Attributed statistics. A number without a source carries less weight in AI extraction. “73% of buyers research on AI tools before purchasing” is weaker than “according to the 2024 Demand Gen Report, 73% of B2B buyers use AI tools during research.” Write every statistic with inline attribution, at the point where you use it. Not in a reference list at the bottom of the page. AI engines parse inline citations as a quality signal.

That citation habit also pays off on its own: the Princeton GEO study found that citing sources clearly within content improved AI citation rates by 24.9%, independent of the statistics themselves.

Quotable statements. These are sentences that work completely out of context. Short. Declarative. Unambiguous. “GEO (Generative Engine Optimization) is the practice of structuring content so that AI search engines extract and cite it in their responses” is a quotable definition. A four-clause sentence with three conditionals is not. Write 2-3 of these per major section. They are the sentences Perplexity and ChatGPT lift directly into their answers.

Clear definitions. Every important term should have a one-sentence definition that stands alone, the first time you use it. Keep definitions under 25 words. AI engines treat defined terms as extractable units, and users who ask “what is [X]” get routed to pages that define it cleanly.

Structured Data: The GEO Step Most Sites Skip

Structured data tells AI crawlers exactly what type of content they are reading and how to interpret it. FAQPage schema surfaces Q&A pairs as ready-to-extract units. HowTo schema signals sequential steps. Organization schema with sameAs properties helps AI engines build a coherent picture of your brand entity across the web.

The schema types that matter most for GEO right now:

The Organization schema with sameAs is the most underused of these by far. Connecting your brand entity to your LinkedIn company page, Wikidata entry, and Wikipedia page (if you have one) helps both Google’s knowledge graph and the underlying models build a coherent picture of who you are. A brand with a linked entity gets cited more confidently than one that exists only as a domain name.

Implement all of this in JSON-LD, not microdata. JSON-LD lives in the page head, parses cleanly for both traditional crawlers and AI retrieval systems, and you can update it without touching your page markup.

Build Your Presence on the Sources AI Engines Actually Trust

AI engines do not only read your website. They pull from a specific ecosystem of trusted sources, and your brand needs presence across those sources to be cited consistently. Wikipedia, Reddit threads, industry review sites, YouTube transcripts, and LinkedIn articles all feed into what AI engines know about you, separate from anything on your own domain.

From what I observe auditing sites across industries, the sources that appear most reliably in AI-cited footnotes include:

• Wikipedia and Wikidata (entity recognition, core fact sourcing)
• Reddit and Quora (authentic user opinions, real-world use cases that AI engines find credible)
• G2, Capterra, Trustpilot (especially for software and professional services)
• YouTube with transcripts enabled (Google AI Overviews increasingly surfaces video content)
• LinkedIn company pages and LinkedIn articles
• Mentions in sector-relevant press and trade publications

I want to be honest about the limits here: Google has not published an exact specification of which sources feed AI Overviews retrieval versus training data. The pattern I see across audits is that brands with active presence on four or more of these sources consistently outperform brands that only optimize their own site. That is pattern recognition across projects, not a published spec. Take it as directional, not definitive.

The practical move: audit each source for your brand name right now. Fix any incorrect information first. Then build presence systematically on the gaps. An active Reddit presence where you genuinely help users is worth more for GEO than a dozen thin blog posts. Earned mentions build trust. Manufactured ones do not.

How to Measure Whether Your GEO Work Is Actually Moving the Needle

Share of Model is the GEO metric that replaces traditional keyword rankings. It measures what percentage of AI responses about your category mention your brand, relative to competitors. Track it by running your core queries through multiple AI engines every two weeks, recording mention rates, and watching the trend line as you execute GEO changes.

The tooling here is still maturing, to put it diplomatically. Semrush’s AI Toolkit tracks AI brand mentions over time. Brand24 captures some AI mentions as well. Neither is comprehensive. Both give you directional trend data without querying every engine manually every week.

The manual method is slower. I still recommend running it alongside any tool, because reading actual AI responses catches nuances dashboards miss: not just whether you were mentioned, but how you were mentioned, and next to which competitors. Set a recurring calendar reminder every two weeks to run your 15-query audit, update the spreadsheet, and calculate your mention rate.

Did your mention rate go from 2 out of 15 queries to 5 out of 15 over the past month? Document what changed between those two checkpoints. That is how you start understanding which GEO actions actually move Share of Model for your specific category.

A secondary metric worth tracking: AI referral traffic in Google Analytics 4. As of 2025, you can filter sessions by source to see direct traffic arriving from ChatGPT.com, Perplexity.ai, and Gemini. It is small for most sites right now. The trend line matters more than the absolute number at this stage.

GEO results move slowly at first, then compound. Three months of consistent work across these six steps typically shows measurable Share of Model improvement. Six months often shows it in GA4 referral data as well.

If you want a baseline measurement done for you before starting this process, Hingewise offers a GEO Snapshot that maps your current AI visibility across the major engines and identifies which gaps to close first. It is the starting point we use for every client engagement.

Want to see how your brand shows up across AI? Get a free GEO Snapshot.