How AI Decides Which Products to Recommend | Flozi

Search for this question and most results explain how on-site recommendation engines work: collaborative filtering, purchase history analysis, behavioral data, the system behind "customers also bought." That's a useful technology, but it's not what this article is about.

This article is about a different question: when a buyer types "best running shoe for flat feet" into ChatGPT, or asks Perplexity for "the best collagen supplement for joint pain," how does AI decide which brand names to put in that answer?

Those are two completely different mechanisms. One lives inside your store. The other lives inside the AI system, runs before the buyer ever reaches your store, and determines whether they reach you at all.

The two stages that determine every AI product recommendation

When a buyer asks an AI system for a product recommendation, two things happen in sequence.

Stage one: retrieval. The AI queries an index of web content — pages, articles, reviews, forum posts, editorial roundups — and pulls back the passages most relevant to the query. Your Shopify product pages, your blog posts, reviews of your products on third-party sites, mentions in editorial content: all of this is potentially in that retrieval pool. Whether any of it actually gets pulled depends on whether AI can clearly read it and extract relevant information.

Stage two: generation. AI takes the retrieved passages and constructs a response. This is where brand names get selected. AI may have retrieved content from dozens of sources. It synthesizes them into an answer, and the brands that appear in that answer are the ones the retrieved content most clearly and consistently pointed to.

The important thing to understand: a brand can fail at either stage. Retrieval failure means AI couldn't extract useful information from your pages or they weren't retrieved at all. Generation failure means AI retrieved information about you but didn't have enough consistent signal about your brand to name you with confidence. Both failures look the same from outside — you're not in the answer.

For a technical breakdown of this process, see How LLM Search Works.

What retrieval actually evaluates

Retrieval is not keyword matching. AI isn't looking for your target keyword to appear on your page a certain number of times. It's evaluating whether your page contains clear, extractable information that's relevant to the query.

For a product query, the relevant information is specific: what the product is, what it does, who it's for, what makes it different, what buyers say about it. AI is pulling the passages that answer those questions most clearly, from whichever sources answer them best.

This is why a Shopify product page that opens with brand story and stacks adjectives ("premium, luxurious, sustainably crafted") can get passed over in favour of a page that leads with concrete product facts ("fragrance-free barrier repair cream, ceramide-rich formula, for sensitive and eczema-prone skin"). Both pages are about similar products. One gives AI something to extract. The other doesn't.

A few specific factors that affect retrieval for product pages:

Structured content. AI retrieves passages, not full pages. Content that's clearly structured into specific, answerable sections gets extracted more cleanly than content that reads as continuous persuasive prose.

FAQ blocks. AI builds answers from question-answer patterns. A product page with a FAQ section — even a short one covering fit, ingredients, use case, and common comparisons — provides exactly the format AI is looking for.

Schema markup. Product, Review, and FAQ schema give AI structured data that supplements what it can read from your page text. Product schema in particular explicitly signals what the product is, who makes it, and what it costs — the basic facts AI needs.

On-page reviews. Customer reviews contain buyer language: the specific words buyers use to describe who the product worked for and what problem it solved. That language often matches closely how other buyers phrase their queries. Reviews that are accessible in the static HTML of your page (not loaded dynamically via JavaScript) are an extraction source AI can use.

What generation evaluates

Getting into the retrieval pool is necessary but not sufficient. When AI moves from retrieval to generation — from gathering information to constructing the answer — it makes decisions about which brands to name. Brands with stronger, more consistent signals get named. Brands with weaker or mixed signals get passed over even if their pages were retrieved.

The signals that matter at the generation stage are different from retrieval signals. Generation is evaluating brand authority: how much confidence AI has in naming this brand in response to this query.

Consistency across your own site. AI reads your homepage, your About page, your product pages, your blog. If those sources describe your brand differently — different category, different audience, different positioning — AI gets conflicting signals about what you are. Inconsistency at this level lowers AI's confidence in naming you.

Third-party corroboration. Editorial mentions, press coverage, expert endorsements, review sites: these tell AI that independent sources outside your own website agree on what your brand is and what category it belongs to. A brand that appears consistently across multiple credible sources in its category has higher authority than one that primarily exists on its own site.

Category specificity. AI looks for whether your brand is clearly associated with a specific category. A brand that signals "we make running shoes for overpronators" builds stronger authority for that specific query type than a brand with broad, shifting positioning. Category ownership is a generation signal.

Content that demonstrates expertise. Educational content your brand has produced — buying guides, ingredient explainers, how-to articles — contributes to AI's model of your brand as a credible source in your category. This doesn't require a large content operation. A small number of well-structured pieces on topics your category buyers care about build meaningful authority over time.

Why this is different from how Google ranks products

Google's ranking algorithm gives significant weight to domain-level signals: backlinks, page authority, site reputation built up over time. A product page can rank well because the domain it sits on has accumulated substantial authority, even if the page itself has thin content.

AI doesn't work this way. Retrieval evaluates the content of the page directly. A product page on a high-authority domain with a vague, adjective-heavy description can get passed over by AI in favour of a product page on a lower-authority domain that clearly answers what the product is and who it's for.

This is why brands with strong Google rankings are often surprised to find themselves absent from AI product recommendations. The signals that built their search rankings don't translate directly to AI visibility. For a full breakdown of where the two systems diverge, see Shopify AEO vs SEO.

The practical implication for Shopify brands

AI has already built a model of your product category. It knows which brands are typically recommended for which query types, which sources it trusts in your vertical, and how clearly different brands' pages describe what they offer.

When a buyer asks a product question in your category, AI draws on that existing model. Brands that are clearly represented in it get named. Brands that aren't don't.

The model isn't static. It updates as AI systems re-crawl and re-index web content. Changes you make to your product pages show up in AI answers within 4-8 weeks. Brand consistency and authority signals take longer to build: 8-16 weeks is realistic for owned-site changes to settle, longer for third-party coverage to accumulate.

The starting point is knowing where you currently stand. Run the queries your buyers use in ChatGPT, Perplexity, and Google AI Overviews. Note which brands appear and what the cited sources are. That tells you both whether you're in AI's model and what that model currently says about your category.

For the specific changes that improve your retrieval and authority signals, see How to Optimize Shopify Product Pages for AI Recommendations and AEO for Shopify: The Complete Guide.

Frequently asked questions

Does AI use the same signals for all product categories?
The mechanism is the same across categories but the sources AI trusts vary by vertical. For beauty, editorial sources like Vogue, Healthline, and Byrdie carry significant weight. For supplements, clinical research sources and health publications matter more. For pet care, publications like Dog Food Advisor and vet-endorsed sources. Understanding which sources AI treats as authoritative in your specific category shapes where your brand needs to appear.

Can a smaller Shopify brand compete with established brands in AI recommendations?
Yes, because AI authority isn't purely a function of brand size. It's a function of how clearly and consistently a brand is represented across the signals AI evaluates. A smaller brand with well-structured product pages, consistent brand signals, and some editorial presence in its category can outperform a larger brand with vague product descriptions and mixed signals. The bar is about clarity, not scale.

Does paid advertising affect AI recommendations?
No. AI systems use organic web content to construct recommendations. Ad spend has no effect on retrieval or generation. This is one of the reasons AEO is increasingly treated as a distinct discipline: the levers are different from paid channels and from SEO.

See how AI currently represents your Shopify brand with a Flozi diagnosis