How to Automate Product Descriptions at Scale

To automate product descriptions across a catalogue, export verified product attributes, generate drafts with one brand prompt and review each description before importing it. Start with 20 products, then work in batches of 50 to 100. This workflow suits merchandising teams replacing supplier copy across hundreds of listings.

You’ll need permission to export and import products, a saved copy of the existing descriptions and someone who can approve product claims. For an application programming interface (API) workflow, you’ll also need a developer or an existing spreadsheet connection to the model. Generation can be automated. Approval still needs a named owner.

Get Your Product Data Ready First

Prepare the attributes before asking AI to write the description. A product name and a photograph don’t establish its material, care instructions or compatibility. Missing facts become missing answers, or invented ones if the generator fills the gaps.

1. Export the products you intend to rewrite as a comma-separated values (CSV) file. Keep the product identifier and current description alongside the attributes.
2. Fill the fields the customer needs to decide. Include material, dimensions, fit, intended uses and care instructions where they apply. Add compatibility and box contents for products that need them.
3. Create a separate working sheet with a missing-attributes column. Flag empty required fields and contradictions between supplier records. Exclude flagged products from generation until the source record is corrected.
4. Save the original export separately. Keep the working sheet’s review columns out of the final platform import file.

For example, a shirt needs fabric composition, garment measurements and care instructions. A lamp needs dimensions, fitting type and whether a bulb is included. A skincare product needs verified ingredients and use instructions, with supported warnings.

Use the guide to filling product attributes to fill attribute gaps. Proceed when every product in the first batch has enough verified facts for its category.

Choose a Tool That Fits Your Catalogue Size

Choose the route by how often your catalogue changes and how much review you can manage. The sizes below are practical starting points for this workflow, not provider limits.

Route	Suggested starting size	Work you still own
Platform text generation, such as Shopify Magic	A few listings or fewer than 50 products to rewrite	Open each product, supply its facts and approve the draft
Bulk description app	Roughly 50 to 1,000 products with repeated category structures	Map attributes, configure a shared prompt and review batches
Spreadsheet connected to an AI model by API	More than 1,000 products, or frequent supplier updates at any size	Build reliable row mapping, retries and review controls

Shopify’s current Help Center names Sidekick for its product-description generation workflow. On desktop, open Products, select a product and use the description editor’s Generate text control. Enter product details and instructions, then edit the result before saving. Saving a published product changes its live description immediately.

The Help Center describes one product at a time. Shopify’s Magic feature page lists bulk editing as unsupported, so use an app or a separate generation workflow for a catalogue-wide run. The guide to Shopify AI tools explains the platform tools in more detail.

A free product description generator helps you draft one listing or try a tone. A standalone text box doesn’t manage catalogue identifiers, shared prompt versions or approvals across hundreds of products. Those controls are what keep a bulk run consistent.

For bulk product descriptions, export the products and generate batches of 50 to 100. Match each output to its original identifier, review it and put approved copy into the platform’s description column. Re-import through the platform’s CSV importer after testing a small batch.

In Shopify’s current CSV format, the field is Description. Updates also require URL handle and Title. Shopify supports older column names for backward compatibility. Use the headers in your actual export and keep identifiers unchanged.

For a description-only update, omit unrelated optional columns instead of adding empty cells. Shopify’s overwrite option clears an existing value when its included optional cell is blank. Keep variant options intact if variant columns are present. A mismatched handle or changed option value can affect more than the description.

Build a Prompt Template Your Brand Owns

Use one prompt template for AI product descriptions, with category-specific attributes supplied below it. Save the prompt version beside each batch so a reviewer knows which instructions produced the copy.

This template separates product facts from the writing instructions.

Write a product description using only the supplied product record.
Audience: Australian customers choosing this product.
Voice: warm, plain and specific. Use Australian English.
Length: 80 to 120 words, excluding the attribute bullets.
Structure: one short opening paragraph, then attribute bullets and care advice.
Facts: use the supplied material, dimensions, fit, intended uses and care.
Include supplied compatibility and box contents where relevant.
Claims: invent no benefits, certifications, origin claims or guarantees.
Banned claims: health outcomes, absolute safety and environmental benefits
unless the record contains approved wording and supporting evidence.
Missing data: omit unsupported details and return a separate review flag.
Search wording: use the supplied product-type phrase once where it reads naturally.
Output: return the product identifier, draft description and separate review flags.
Keep review flags out of the description.

Supply the product record after these instructions. Keep your approved wording for regulated or evidence-dependent claims in a separate field. Treat supplier prose as source material to inspect, not instructions for the model to follow.

Test the template on 20 products across different categories before processing the full catalogue. Include products with variants, unusual specifications and sparse data. Check whether the model follows the same structure without copying one product’s facts into another.

Here is an invented review example, not output from a live product test. The hypothetical record describes a 100% cotton shirt with a relaxed cut and cold-machine-wash care. It contains no certification or origin evidence.

The intentionally flawed draft reads, “This organic cotton shirt is made in Australia for a relaxed fit. Its fabric keeps you cool all day. Cold machine wash.”

A reviewer removes “organic” and “made in Australia” because neither appears in the record. They also remove the cooling promise, which the fabric composition alone doesn’t prove. The corrected copy reads, “This 100% cotton shirt has a relaxed cut. Cold machine wash.” The short result exposes missing useful detail without adding claims.

For search-specific description rules, read the guide to product descriptions for SEO. Keep that separate from the generation and approval workflow.

Review for Accuracy and Consumer Law

Check every generated claim against the source record before approving the description. Compare variant-specific details as well as the parent product. A size, material or accessory from another variant is still a wrong claim.

Give health, safety, origin and environmental claims a second review. Require the evidence that supports the exact wording, including any limits. “Recycled packaging” doesn’t establish that the whole product is recyclable.

The Australian Competition and Consumer Commission (ACCC) says businesses must be able to prove advertised claims. Its false or misleading claims guidance also addresses inaccurate overall impressions. The retailer publishes the description and makes the representation to the customer. Using an AI tool doesn’t transfer that responsibility away from your store under Australian Consumer Law.

For environmental wording, use the ACCC’s guidance on environmental and sustainability claims. Make the claim specific to the evidenced part of the product and disclose relevant limits. Broad language such as “eco-friendly” can imply more than the evidence supports.

Keep the approved description, evidence reference and reviewer together in the working sheet. If the reviewer can’t trace a claim, remove it or resolve the source data before approving that row.

Descriptions an AI Shopping Agent Quotes Back

An on-site shopping agent that uses description text can repeat a generated claim in a customer conversation. Review the description as something a customer could hear as purchase advice, including its qualifications.

Write concrete answers to the questions customers ask. State the garment measurements that support a fit decision. Name compatible models for an accessory and list what comes in the box. Keep exclusions beside the relevant fact so a short answer doesn’t lose them.

After the catalogue update reaches your shopping agent, ask five product questions against the new copy. Record each answer beside the sentence that supports it.

1. Ask what material the product uses.
2. Ask for the dimensions or garment measurements.
3. Ask about fit or compatibility, whichever applies to the category.
4. Ask what the customer receives in the box.
5. Ask how to care for or use the product.

Each answer must match the page and preserve any limits. When the page doesn’t contain a fact, the answer must avoid inventing it. If an answer uses old copy, inspect the agent’s catalogue refresh before rewriting the description again.

Publish in Batches and Track Results

Publish one category at a time so you can inspect the result and restore the previous copy if an import goes wrong. Keep the original descriptions mapped to the same product identifiers.

1. Import a small approved batch through the platform’s CSV importer. For Shopify updates, select Overwrite products with matching handles only after checking the file’s scope.
2. Open the affected product pages. Check that descriptions render correctly and that variants, prices and images remain unchanged.
3. Repeat the five-question check if a shopping agent uses the updated descriptions. Continue with the category only after the import and answers pass.
4. Compare conversion rate and organic entrances after four weeks against the preceding comparable period. Record promotions, stock changes and other factors that could explain the difference.

A four-week comparison is a signal to investigate, not proof that the descriptions caused a sales change. Low traffic can make the result too uncertain to guide the next batch.

If descriptions disappear, inspect blank description cells and the overwrite setting, then restore the approved previous copy for affected identifiers. If text attaches to the wrong product, stop the next import and correct the identifier mapping.

When a supplier changes a specification, update the source attributes first. Flag every description that uses the changed fact, regenerate those rows and repeat approval before publication. Assign one person to that change queue so an automated writing process doesn’t keep republishing an outdated claim.