Answer engine and generative engine optimization (AEO/GEO)

This topic explains how AI-powered answer engines and chatbots discover, cite, and represent your store, and which StoreConnect features support that.

AI-powered search and chat tools such as ChatGPT, Google’s AI Overviews, Perplexity, and Claude increasingly answer shopping questions directly, sometimes without a customer visiting a website at all. Generative Engine Optimization (GEO), also known as Answer Engine Optimization (AEO), is the practice of improving your store’s chance of being the source these tools cite. See SEO and site data for the traditional search-ranking counterpart.

Start from your SEO foundation

Most answer engines build their visibility on top of standard search fundamentals rather than a separate AI-specific ranking system. Before adding anything AI-specific, confirm the basics are in place:

• Page, product, and article titles and meta descriptions are set to accurate, specific values. See SEO and site data.
• Product pages render valid structured data. See Structured data for products.
• Your sitemap and canonical URLs are current and accurate. See Manage the sitemap for your store.

These are the same fundamentals search engines use for ranking, and they are also what most answer engines rely on to find and correctly represent your content. There is no separate, dedicated GEO ranking system to configure instead.

Serve custom feeds for AI tools and answer engines

The most direct AEO/GEO lever StoreConnect exposes is llms.txt: a plain-text file giving AI tools structured context about your store, served through a content page rather than a dedicated feature. It is not limited to catalog data: a typical llms.txt also covers store policies, shipping and returns, and customer support, alongside your products and categories. See Generate an llms.txt file for your site for setup.

llms.txt uses the same underlying mechanism as any other content page with a file extension in its Path field: the page renders raw, Liquid-processed content instead of themed HTML. This mechanism is not specific to llms.txt. You can use the same Page-based approach to serve any other on-demand feed a particular AI tool or answer engine expects, in whatever format it requires, without waiting for a dedicated StoreConnect feature for that tool.

You are not limited to a single feed. Each feed is an independent Page record with its own Path and Content Body, so you can create as many as you need, for example:

• A short llms.txt index at your store root, alongside a more complete companion file such as llms-full.txt. This matches how the llms.txt standard is commonly used in practice, and both can render from Liquid rather than being maintained as separate static files.
• A topic-specific feed for any aspect of your store, not only products: products.txt for your catalog, articles.txt for your blog and help content, or a feed for policies, FAQs, or store information, if a single combined file becomes too large or general.
• A differently-formatted feed for a specific AI tool that expects its own file name or structure, such as a JSON feed served through a .json Path, without changing your general llms.txt.

Link between them the same way you would link any other content, for example referencing llms-full.txt from within llms.txt.

AI crawler access

An answer engine can only cite your store if it can reach your content. Confirm your robots.txt configuration allows the crawlers below, if you want your store to be discoverable by that tool.

Anthropic documents three separate crawlers (ClaudeBot, Claude-User, and Claude-SearchBot), each controllable individually in robots.txt, and states that all three respect robots.txt directives. xAI publishes less webmaster-facing detail for Grok, and reported behavior is inconsistent, so confirm the current guidance in xAI’s own documentation rather than relying on this row alone. Because vendor crawler details change, treat the official links below as the source of truth over this table.

Official vendor documentation, for the current details behind this table: OpenAI’s bots, Google’s AI optimization guide, Anthropic’s web crawlers, and xAI’s web search tool.

:::note Google’s own AI-optimization guidance states that no special llms.txt file or AI-specific markup is required for AI visibility; it treats AI visibility as substantially a byproduct of standard SEO and content-quality fundamentals. Treat structured data and llms.txt as helpful, supplementary levers on top of that foundation, not as a checklist of mandatory requirements. :::

Verify your store’s AI visibility

There is no built-in StoreConnect report for AI referral traffic or answer-engine citations. To check your progress:

• Ask the target AI tool a specific question a customer might ask about your store or products, and check whether it cites your store.
• If your team has visibility into your domain’s request logs, for example through your DNS or CDN provider, check for the crawler user agents listed above to confirm they are reaching your store.