---
title: 'Your first workflow'
description: 'Create related catalog data and verify it through Studio and GraphQL.'
---

# Your first workflow

Complete a framework quickstart and sign into its Studio with write access. Use fictional data in a development project. The tutorial model contains Products and Categories; Products include an image relationship and scoped name and price fields.

## Create a category and product

1. Open **Desk → Categories → Create**. Enter `Accessories` and a slug such as `accessories`, then **Save**.
2. Open **Products → Create**. Enter `Canvas bag`, SKU `BAG-001`, and a price of `24`.
3. Select the Accessories category in the relationship field and save the product.
4. Close and reopen the product. Confirm the values and category persisted.

A relationship selects another record. Disconnecting it and deleting the category are different operations. See [relationship editing](/docs/studio/desk/relationships).

## Add an image

Open [Asset Manager](/docs/studio/asset-manager), upload a synthetic product image, and locate it by filename. Select that image in the product editor’s image field and save. Uploading an asset alone does not attach it to a product.

## Work with variants

After adding a second [scope](/docs/schema/scopes), switch to it and change a local field such as the product name. Save, then compare both scopes. Global fields such as SKU remain shared. Learn the difference between inherited and explicit values in [scoped content](/docs/studio/desk/scoped-content).

## Verify the API result

Open [Explorer](/docs/studio/explorer). Use its schema documentation to select the products query and request the record ID and configured fields. Run the query and compare the returned ID and values with the saved record. Generated names and local-field shapes depend on the deployed schema; use [GraphQL queries](/docs/api/graphql/queries) to construct the operation.

If the result is missing, check the selected project, scope, permissions, and whether the schema deployment completed. Keep this small dataset for later automation and extension exercises.


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