How to Do Generative Engine Optimization (GEO)

What GEO is — and what it isn’t

GEO is how you earn a place inside AI-generated answers, not how you climb a list of blue links. For the full definition, the engines it covers, and how it relates to traditional search, see the generative engine optimization pillar — this page stays focused on the “how.” In short: GEO is not a hack, not a way to “buy” a mention, and not the same thing as ranking on Google. It’s the disciplined work of making your content the most quotable, parseable and trustworthy source for the questions your buyers ask AI.

It’s also not magic you do once. The engines change their selections, competitors publish, and your own pages age. So GEO is a strategy you run continuously, the same way SEO became an ongoing practice rather than a single optimization. Because it’s ongoing, many teams face the GEO agency vs GEO tool decision early on — whether to outsource the work or run it in-house with software — and this guide assumes you’re doing the work yourself.

How to do GEO: 7 steps

Here is a concrete GEO strategy you can follow end to end. Each step builds on the last: you can’t improve what you haven’t measured, and you can’t measure improvement without first knowing your baseline.

1. Audit your current AI visibility

   Before optimizing anything, establish a baseline. Run a scan to see whether you’re mentioned or cited across ChatGPT, Perplexity, Gemini and Google AI Overviews for the questions that matter in your category. Capture which competing sources win the citation when you don’t — that gap is your starting map. CapstonAI’s free scan at app.capston.ai/audit returns a per-engine, per-prompt baseline so you’re optimizing against evidence, not guesswork.

2. Map the prompts that matter

   AI answers are triggered by questions, so list the prompts your buyers actually type: category questions (“best X for Y”), comparison questions (“X vs Z”), and problem-first questions (“how do I fix…”). Prioritize the ones with clear buying intent over vanity queries. This prompt map becomes the test set you scan against and the brief for the content you’ll write.

3. Make your content answer-first and quotable

   Engines lift self-contained passages, so put a direct, complete answer near the top of each page — then expand below it. Write in clear, factual sentences a model can quote without surrounding context. The research backs this up: Aggarwal et al., “GEO: Generative Engine Optimization” (KDD 2024), found that adding cited sources, quotations and statistics increases a page’s likelihood of being surfaced in generative answers.

4. Add structure and schema

   Make your page trivially easy to parse. Use descriptive headings, short paragraphs, bulleted lists and comparison tables so an engine can isolate the exact fact it needs. Add valid, relevant structured data — Article, FAQPage, HowTo, Product where appropriate — so machines read your content the way you intend. Clean structure plus schema removes the friction that keeps you out of answers.

5. Publish and maintain an llms.txt

   Add an llms.txt file at your domain root to signpost AI crawlers toward your most citable pages — your definitions, comparisons and authoritative guides. Keep it current as you publish, so the file always points to your best, freshest sources. Treat it like a curated index for machines, not a dumping ground for every URL.

6. Strengthen entity signals and authority

   Engines cite sources they can confidently identify and trust. Use consistent naming for your brand, products and people everywhere they appear; keep a clear About page; and reinforce it with structured data so your entity is unambiguous. Back claims with cited, reputable sources and earn mentions from sites engines already trust — authority is what tips a mention into a citation.

7. Measure citations and iterate

   Re-scan your prompt map on a schedule and watch how mentions and citations move over time. Treat any single run as noisy — engines vary their outputs — so act on trends across repeated scans, not one snapshot. CapstonAI tracks this over time and its agents for WordPress, Shopify, Drupal and Chrome help you action the gaps you find, closing the loop from measurement back to change.

Common GEO mistakes

Most failed GEO efforts share the same few errors. Avoiding them is often worth more than any single optimization:

• Optimizing before measuring. Changing pages without a baseline means you can’t tell whether anything improved. Scan first, always.
• Burying the answer. If a reader (or model) has to scroll past preamble to reach the point, the engine often won’t lift it. Lead with the answer.
• Reacting to a single scan. One run showing you uncited isn’t a verdict. Engines are probabilistic; act on the trend across repeated scans.
• Inventing authority. Unsupported claims, fake statistics or inconsistent entity naming erode the trust signals engines rely on. Cite real sources and stay consistent.
• Measuring without acting. A dashboard that turns red still needs someone to fix the structural cause — content, schema, llms.txt or entity signals.

How to measure whether it’s working

GEO success isn’t a ranking position; it’s whether engines name and cite you for the prompts that matter. So measure the same things you scanned in step 1, then watch them over time. The metrics that count are: how often you’re mentioned in answers, how often you’re cited with a link, your share of voice against named competitors, and which content types engines pull from. Improvement looks like a rising share of your priority prompts returning a mention or citation across repeated scans.

Because outputs vary run to run, set a cadence — re-scan on a fixed schedule and compare windows rather than days. When you publish a new answer-first page or ship a schema fix, check whether the prompts it targets start surfacing you in subsequent scans. That feedback loop is the whole point of GEO as a strategy: publish, measure, learn, repeat. To see how the loop plays out in practice, our GEO case studies show how brands moved from uncited to cited over successive scans. If you’re weighing how this differs from classic search work, the practical contrasts are laid out in GEO vs SEO, and the tooling options are compared in our guide to GEO tools.

Frequently asked questions