E-commerce & DTC5.0 · 0 ratings

Shopify Store Information Architecture Planner

Designs collection structure, navigation, and filtering for a catalog so shoppers find products in fewer clicks.

Role-BasedStructured-OutputStep-by-Step

Prompt

ROLE: You are an e-commerce UX architect who structures Shopify catalogs for findability and SEO.

CONTEXT: Brand: [BRAND] selling [PRODUCT_RANGE]. Catalog size: [NUMBER_OF_SKUS]. Customer mental models / how they shop: [SHOPPING_BEHAVIORS]. Main categories today: [CURRENT_CATEGORIES]. SEO priority terms: [TARGET_KEYWORDS].

TASK:
1. Propose a top-level navigation (max 6 primary items) reflecting how customers actually shop, not how we're organized internally.
2. Design the collection hierarchy (collections + sub-collections) and the rule that assigns products to each.
3. Recommend product filters/facets (e.g., size, use case, price) and their values.
4. Map [TARGET_KEYWORDS] to specific collection pages for SEO, including a one-line meta description per page.
5. Flag any merchandising rules (featured order, new-in, bestsellers logic).

OUTPUT FORMAT: Nav tree (indented) | Collection table (Collection | Assignment rule | Target keyword | Meta) | Filter spec.

CONSTRAINTS: No more than 3 levels deep. Every collection must map to a real shopper intent. Avoid orphan products - ensure each SKU lands in at least one collection.

How to use this prompt

  1. 1

    Copy the prompt above and paste it into ChatGPT, Claude, or Gemini — or open it in the visual Studio to edit each part on a canvas and run it with your own key.

  2. 2

    Replace any bracketed placeholders with your specifics. The more concrete your context and constraints, the sharper the result — see the 5-part prompt structure.

  3. 3

    Run it, then refine. Ask the model to critique and improve its own answer with self-critique prompting.

Techniques in this prompt

Role-Based

Assigns the model an expert persona so it adopts the right vocabulary, depth, and standards for the task.

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Structured Output

Pins the response to a defined structure so it drops straight into your workflow.

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Step-by-Step

Forces explicit intermediate reasoning instead of jumping to a conclusion, which improves accuracy on hard tasks.

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