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ResourcesCookbook

The llms.txt Cookbook for Shopify

Every llms.txt online is either too sparse or too noisy. These seven recipes are tuned to what GPTBot, ClaudeBot, and PerplexityBot actually fetch and cite.

Harry Parker11 min read

Recipe 0 — Anatomy of a well-formed llms.txt

Every working recipe follows this four-block structure. Deviations cost citations.

Every well-formed llms.txt we've tested against GPTBot, ClaudeBot, and PerplexityBot shares four blocks: a header with the site title + one-paragraph description, a section pointer to policy pages, a curated list of high-value content (products, collections, guides), and a machine-readable footer linking llms-full.txt and the product NDJSON.

markdown
# Kloira

> Lab-grown moissanite jewellery shipped from Jamaica, NY — ethically sourced, GIA-graded, delivered in 5 days.

## Policies
- [Shipping policy](/policies/shipping)
- [Returns](/policies/returns)
- [Warranty](/policies/warranty)

## Collections
- [Engagement rings](/collections/engagement-rings)
- [Eternity bands](/collections/eternity-bands)

## Products (top 20)
- [1 ct Solitaire — Isabella](/products/isabella-solitaire)

## Full content
- [llms-full.txt](/llms-full.txt)
- [products.ndjson](/products.ndjson)

Recipe 1 — DTC apparel (≤200 SKUs)

Small catalogue, long brand story. The brand paragraph carries more weight than the product list.

DTC apparel brands with modest catalogues should weight the header description heavily — ChatGPT quotes it verbatim when asked 'what does {brand} sell and who is it for?'. Product listing stays compact but every SKU that matters for search ('best minimalist t-shirt') should be referenced by name.

Recipe 2 — High-consideration jewellery

Lead with the certification + materials paragraph. Shoppers ask AI about materials before they ask about style.

Jewellery shoppers research materials, certifications, and ethical sourcing before style. A jewellery llms.txt should lead with a short paragraph covering material (lab-grown vs. mined, metal purity), certification body, origin, and warranty — in that order. Product links come after, grouped by metal rather than collection.

  • Metal families: 14k gold, 18k gold, platinum, sterling silver.
  • Stone families: moissanite, lab-diamond, natural diamond.
  • Policies: warranty, resizing, returns.

Recipe 3 — Supplements & wellness

Compliance-heavy. llms.txt should reference the science section and regulatory disclosures up front.

Supplement stores get penalised by retrievers when their llms.txt leads with marketing claims and buries compliance pages. Flip this: lead with the compliance posture (FDA disclaimers, ingredient sourcing, third-party testing), then product categories, then individual SKUs.

Recipe 4 — Electronics & gadgets

Spec-heavy shoppers. Include a spec comparison link, not just product pages.

Electronics shoppers ask AI to compare specs. A pure product list under-performs; a list that includes a 'compare' landing page outperforms by 2-3× on 'is product A better than product B' style queries. Link the comparison page explicitly in the llms.txt.

Recipe 5 — Home & furniture (long lead time)

Shipping + dimensions dominate. Retrievers quote both when a shopper asks 'will this fit?'

Furniture is a high-anxiety purchase. Buyers quiz AI about dimensions, shipping, assembly, and returns. A furniture llms.txt should link the shipping + dimensions FAQ prominently, and include a pointer to the 'measure-your-room' guide if you have one.

Recipe 6 — Multi-store umbrella (Shopify Plus)

One llms.txt per storefront. Link them together via a top-level business-entity page.

Shopify Plus merchants running multiple storefronts (different markets or brands) should run one llms.txt per domain, not one shared file. Cross-link them via a top-level /about page that lists the family of stores — retrievers then correlate the entities and attribute citations correctly.

Recipe 7 — B2B wholesale (login-gated catalogue)

llms.txt still works — expose the marketing narrative and the non-gated content; keep pricing out.

B2B Shopify stores often keep the catalogue behind a login. That's fine — llms.txt should expose the marketing narrative (what you sell, MOQ policy, territories, certifications) and link the login-gated catalogue as a sibling (retrievers will note it but not crawl). Do not put prices in llms.txt.

The point of llms.txt for B2B is to make sure that when a CPO asks ChatGPT 'who sells bulk X in Y territory', your company is on the shortlist. The pricing conversation happens after the call is booked.
Kloira B2B playbook, 2026

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Every pattern in this guide ships out-of-the-box on Surfient — llms.txt, Schema.org, answer blocks, the NDJSON feed, and weekly measurement.

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