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Fundamentals · 4 min read

What is generative engine optimization (GEO)?

GEO is the practice of making your content and product data legible to AI answer engines. Here is what it actually involves, how it differs from SEO, and what nobody can promise you.

Generative engine optimization (GEO) is the practice of structuring your content and product data so AI answer engines can find it, understand it, trust it and quote it. It overlaps heavily with technical SEO, but the target has changed: instead of competing for a blue link on a results page, you are competing to be the source an assistant draws on when it composes an answer.

Why a new term at all

Classic search returns a list and lets the user choose. A grounded answer engine can compose a response and attach source URLs to specific claims. That behavior is documented for OpenAI web search, Gemini Grounding with Google Search and Perplexity Sonar. The number and selection of sources are provider- and query-dependent.

That changes what matters:

  • Extractability beats keyword density. A model needs a self-contained passage it can lift. A page that only makes sense after three paragraphs of preamble is a poor candidate.
  • Machine-readable facts beat prose claims. "Waterproof, 280g, rock plate" as structured attributes is more useful than the same facts buried in a paragraph.
  • Identity beats branding. A GTIN tells a machine that your listing and a review elsewhere describe the same object. A tagline does not.
  • Corroboration matters. Answer engines prefer facts they can see confirmed in more than one place.

What GEO actually involves

1. Crawl access

Nothing else matters if the crawler cannot fetch the page. That means checking robots.txt for rules covering AI user agents specifically — GPTBot, OAI-SearchBot, PerplexityBot, Google-Extended, ClaudeBot — plus noindex directives and X-Robots-Tag headers.

A surprising number of stores discover they blocked an AI crawler years ago and forgot.

2. Structured data completeness

For ecommerce, that means Product markup with complete, accurate commerce facts. Google's Product documentation and Schema.org's Product vocabulary define fields including name, description, image, SKU, GTIN, brand, offers, price, currency, availability and ratings.

Partial markup is common and quietly expensive. A Product node with no brand and no gtin cannot be reliably matched to anything else on the web about that item.

3. Content that answers a question

Answer engines quote passages. The passages worth quoting are specific, factual and self-contained:

The Trail Runner GTX weighs 280g in a men's UK 9, uses a Gore-Tex membrane, and includes a rock plate under the forefoot. It suits wet, technical trails rather than road running.

That is quotable. "Our best-ever shoe, engineered for performance" is not.

4. Merchant trust signals

A store with a published business identity, returns policy, support contact and shipping terms gives shoppers and machines concrete merchant information. Google recommends clear return, shipping and customer-support information and supports shipping and return properties in merchant markup.

How GEO differs from SEO in practice

| | Classic SEO | GEO | |---|---|---| | Goal | Rank in a list | Be cited in an answer | | Unit of value | Page | Passage and structured fact | | Result shape | Ordered result list | Composed answer with provider-selected sources | | Keyword role | Central | Secondary to factual completeness | | Measurement | Rank tracking | Prompt-level mention and citation tracking |

They are not opposed. Almost everything that helps GEO also helps traditional search, because both ultimately reward pages that are accessible, factually complete and genuinely useful.

What nobody can promise you

Be sceptical of any vendor — including us — that promises placement in ChatGPT, Gemini or Perplexity results. Three reasons:

  1. The platforms control retrieval. They change models and ranking without notice.
  2. Answers are not deterministic. The same prompt run twice can produce different sources.
  3. APIs are not the consumer apps. Monitoring through official APIs is the honest, terms-compliant way to measure, but it does not reproduce a signed-in user's personalised experience exactly.

What can be promised is measurement and improvement of the things you control: whether crawlers can reach you, whether your data is complete, whether your content is quotable, and what the AI systems say today versus last month.

Where to start

If you run a WooCommerce store, in this order:

  1. Check robots.txt and X-Robots-Tag for AI crawler rules you did not intend.
  2. Fill in GTIN and brand for your best-selling products.
  3. Rewrite thin product descriptions so each one answers "what is this, who is it for, when should I pick it".
  4. Add alt text to every product image.
  5. Publish returns and shipping information as real pages, not modal popups.
  6. Start measuring, so step 7 is informed rather than guessed.

Start with a focused product set, record the baseline and measure each change against stored provider responses rather than assuming an outcome.

Primary sources

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