AEO and GEO: the same thing?
In 2026 practice — yes. Both terms describe one discipline; only the emphasis differs.
AEO (Answer Engine Optimization) came first, for optimising towards Featured Snippets, Knowledge Panels and voice search. The emphasis is on the “answer”: the goal is to land inside the ready-made answer the user sees.
GEO (Generative Engine Optimization) arrived with the LLM era. The emphasis is on “generation”: the AI does not pick a ready answer but synthesises a new one from dozens of sources. This is the term Shopify uses in its official guides — Greg Bernhardt, senior SEO specialist at Shopify, describes GEO as a standard part of promoting a store.
In practice you optimise for the same platforms with the same methods: structured data, llms.txt, content strategy. Technical SEO specialists lean towards GEO; marketers and tools (Profound, Otterly, AgentFuel) lean towards AEO. Throughout this series the two terms are synonyms — which one we use depends only on the audience for the article.
How this differs from classic SEO
In classic SEO your site competes for a position in the results. Ten blue links, and the shopper picks one and clicks.
In AEO/GEO your site competes for a mention inside the answer. The AI engine shows not a results page but a finished answer with three to five options and three to five sources. If you are not among those sources, you do not exist for that shopper. If you are, you have just been given shelf space among three options rather than twenty. That converts better than tenth place in Google.
The key practical difference: in SEO traffic is measured in clicks; in AEO/GEO it is measured in mentions.
How this works in Shopify
Shopify is better set up for AEO than most platforms — Schema out of the box, clean URLs, fast rendering.
In May 2026 a key signal was added: Shopify began generating `llms.txt` and `agents.md` automatically at the root of every store. This is a machine-readable index for AI bots and agents — store metadata, links to products and collections, instructions for agents, MCP endpoints for agentic commerce. A month earlier you needed an app for that; now the file works out of the box with no involvement from the owner. Checking is simple: open `yourstore.com/llms.txt` and the file should be there.
But there are two things Shopify will not do for you — and they are what separates “a store that appears in AI answers” from “one of a dozen faceless stores”.
Extended Product schema. The basics ship with themes, but without the key attributes — material, weight, dimensions, warranty, GTIN. Those fields are what separate your product from ten near-identical ones in the eyes of an AI.
Content beyond the catalogue. AI cites comparison guides and FAQs more often than product pages. The blog becomes more important than the catalogue. A product page talks about one product; a guide compares several, and the AI picks the guide as the “useful answer”.
What to do today
First check that you already have an auto-generated `llms.txt` — open `yourstore.com/llms.txt`. If the file is there, Shopify has already produced the basic AEO signal for you. If not, the feature is rolling out in stages; check again in a week.
Then open ChatGPT and ask the three questions your audience asks most — not “buy X” but “how to choose X” or “X or Y”. Look at who the AI cites. If your store is not there, you have an AEO job for the month ahead.
This article is an introduction. The rest of the series covers the mechanics of query fan-out, a formula for product descriptions written for AI, five common mistakes, and the metrics for your dashboard.