---
title: "AI product descriptions and catalog enrichment for ecommerce"
author: "Performetic Ekibi"
url: "https://www.performetic.com/en/blog/ai-product-descriptions-catalog-enrichment"
published: "2026-11-01T12:00:00.000Z"
updated: "2026-11-01T12:00:30.980Z"
---

# AI product descriptions and catalog enrichment for ecommerce

> Writing product descriptions and enriching your catalog with AI saves a lot of time when it runs on solid source data with human review. Clean your product data first, write a template and brand guide, then pilot on a small batch. Google says mass content without added value can count as spam, and Merchant Center needs AI text flagged.

## How do you use AI for product descriptions and catalog enrichment?

AI catalog enrichment works in four steps: clean your product data into one table, write a template for brand voice and structure, have AI generate text and attributes only from that data, then have a person review every output. Start with a pilot of 20-50 products, fix the template, and only then scale up category by category.

We covered how to write a single description in our guide to [SEO-friendly product descriptions](/en/blog/how-to-write-seo-product-descriptions). This post is about scale: how to enrich a catalog of 300, 3,000 or 30,000 products with AI, and how to stay within Google's rules while doing it.

## What does catalog enrichment mean?

Catalog enrichment means filling in missing and weak information in your product records. That's not just description copy; it includes structured fields like colour, material, size, use case, care instructions, compatibility and GTIN. A pattern we see on many stores: products get uploaded from a supplier file with a one-line title and a copied description, filters stay empty, and the same thin data flows into the Google Shopping feed.

Those gaps cost money in three places. Products don't show up in on-site filters, search engines see thin pages, and Shopping ads match the wrong searches.

## Where does AI help with product descriptions, and where doesn't it?

AI is good at reorganising and transforming data it's given. It's prone to inventing information it doesn't have. Google's own Search documentation says so plainly: generative models don't retrieve facts, they predict a likely sequence of words from training data, so output has to be checked.

| Task | Good fit for AI? | What to watch |
|---|---|---|
| Turning a spec sheet into readable copy | Yes | Must not add features missing from the source |
| Rewriting supplier text | Yes | Needs a brand voice template |
| Filling empty colour, material, category fields | Partly | Inferences from images or text need human sign-off |
| Reformatting titles for the feed | Yes | Respect character limits |
| Producing dimensions, ingredients, certifications | No | These must come from the supplier or product |
| Translation and localisation | Yes | Check sizes and units |

## Does Google penalise AI-written product descriptions?

Not for being AI-written, but it can for adding no value. Google's guidance on generative AI content says that using AI tools to generate many pages without adding value for users may violate its spam policy on scaled content abuse. Stamping 5,000 products with the same template and no product-specific facts is risky. Every description needs to carry real information about that product.

The same guidance notes that for ecommerce sites, AI-generated product data must be specified separately and labelled as AI-generated.

## How do you flag AI-written text in Merchant Center?

According to Google Merchant Center's product data specification, titles created with generative AI go in the structured_title attribute and descriptions in structured_description. Setting the digital_source_type sub-attribute to trained_algorithmic_media declares the text as AI-generated. Title content can be up to 150 characters and description content up to 5,000. For AI-generated product images, Google asks you to keep metadata such as the IPTC DigitalSourceType tag.

One more feed rule to build in: Google asks that the description field leave out promotional content such as prices, sale dates, shipping details and your company name. Put that rule in your AI template explicitly, or lines like "Buy now, free shipping!" will leak into the feed. For more on feeds, see our post on [product feed optimisation for Performance Max](/en/blog/performance-max-product-feed-optimization).

## How do you enrich a catalog with AI, step by step?

1. **Collect the source data.** Merge supplier files, spec sheets, existing descriptions and customer questions into one table. AI can't turn bad data into good copy.
2. **List the gaps.** See which attributes are empty for each product; colour, material, size and use case are usually the most common.
3. **Write a template and brand guide.** Include length, tone, title structure, banned phrases and the rule "never add information that isn't in the source data".
4. **Pilot on 20-50 products.** Have one person check output line by line, log the error types and fix the template.
5. **Scale by category.** Tune the template per category; shoes and cosmetics don't get written the same way.
6. **Spot-check batches.** In bulk runs, check random products from every batch.
7. **Measure the result.** Compare organic traffic, filter usage, Shopping impressions and conversion rate in enriched categories against the previous period.

## What should a good AI template include?

The template is the instruction you give the AI, and it decides most of the quality. It should cover who the target customer is, description length, paragraph and bullet structure, title order (brand, product type, key attribute, colour, size), phrases to avoid and, above all, the rule "never add anything that isn't in the source data; if unsure, leave the field empty".

Adding two or three strong example descriptions works better than a long list of instructions. AI tends to imitate examples, so pick them from your best sellers and from copy you genuinely like. Add the questions customer service hears most, too: does it run small, does it fade after washing, which devices is it compatible with. Answering those in the description reduces returns and helps the page show for long-tail searches.

## Which tools can you use?

Your platform's built-in tools may be enough to start. Shopify Magic, for example, generates text suggestions including product descriptions, and according to Shopify's help page its generally available features are offered on all plans and in all languages. For thousands of products, bulk generation through an AI API, a product table and an automation workflow is usually more efficient. Whichever route you take, what decides quality isn't the tool; it's your source data and your review process.

## What does an AI-enriched catalog deliver?

Done well, you get three gains: products become findable in filters, product pages start showing for long-tail searches and Shopping ads match more relevant queries. A pattern we often see is that once feed attribute fields are filled in, Shopping campaigns start getting impressions on more relevant searches. The effect varies by category and competition, so run a before-and-after comparison on your pilot category.

If you'd like to set up catalog enrichment and feed automation on your own stack, see our [smart automation](/en/services/smart-automation) service or request a [free growth audit](/en/contact) from Performetic.

## Key takeaways

- Catalog enrichment covers structured fields like colour, material, size and GTIN, not just description copy.
- AI is good at transforming data and prone to inventing it; source data and human review are essential.
- Google doesn't penalise AI content by default, but mass content without added value can breach its spam policy.
- In Merchant Center, AI text is flagged with structured_title and structured_description set to trained_algorithmic_media.
- Pilot on 20-50 products, scale by category and measure traffic, impressions and conversion.

## FAQ

### Do AI-written product descriptions hurt SEO?

Being written by AI doesn't hurt on its own. The problem is producing many pages from the same template with no product-specific information, which Google can treat as scaled content abuse. Each description should carry the product's real attributes, be checked by a person and genuinely help the shopper decide.

### Do you have to flag AI text in Google Merchant Center?

Google asks merchants to disclose product titles and descriptions created with generative AI. You do this with the structured_title and structured_description attributes, setting digital_source_type to trained_algorithmic_media. For AI-generated images, the IPTC metadata that marks them as AI-made should be kept intact.

### Can AI fill in missing product attributes?

Partly. It can pull attributes like colour, material or use case from existing text or a spec sheet, but those inferences must be checked. Don't let AI generate dimensions, ingredients, certifications or GTINs; those must come from the supplier or the product itself. An incorrect GTIN gets the product disapproved.

### How many products should you start with?

We recommend a pilot of 20-50 products. That's enough to spot template problems and the mistakes AI tends to make. Once you've reviewed the pilot and fixed the template, scale up category by category and keep checking random samples from every batch to hold quality.

## Sources

- [Google Search Central: Using generative AI content](https://developers.google.com/search/docs/fundamentals/using-gen-ai-content)
- [Google Merchant Center Help: Product data specification](https://support.google.com/merchants/answer/7052112)
- [Google Merchant Center Help: Description and structured description](https://support.google.com/merchants/answer/6324468)
- [Shopify Help Center: Shopify Magic](https://help.shopify.com/en/manual/shopify-admin/productivity-tools/shopify-magic)