Mass Translate with ChatGPT in Magento 2: translation, SEO, content moderation, and linking

June 1, 2026 Kowal

A practical article on how to use Mass Translate with ChatGPT in Magento 2 for translation, SEO work, JSON structures, technical terminology, and organizing linking between store views

Mass Translate with ChatGPT for Magento 2 is most easily associated with one task: translating product and category descriptions into additional store views. That is a correct association, but too narrow. In practice, this module becomes truly valuable only when we treat it not as a simple translator, but as a layer for bulk content processing in Magento.

Viewed this way, the same mechanism can handle not only catalog localization, but also:

filling in missing meta data,
organizing SEO fields,
content moderation and normalization,
processing product descriptions according to strict rules,
translating specialized technical content,
editorial operations on blog posts,
replacing internal links after content translation.

This is exactly what the prompts used in GERDA, VITCAS, and Kowal stores demonstrate. Their common denominator is simple: what matters most is not the integration with the model itself, but how precisely we define the task, the data format, and the boundaries of AI activity.

Why Mass Translate in Magento 2 should not be treated like a regular translator

In many stores, the problem is no longer that content needs to be translated. The problem is that it needs to be processed according to specific business logic:

fill in only empty fields,
do not touch existing data,
preserve HTML and placeholders,
do not add new information,
maintain terminology consistency,
distinguish technical fields from user-visible fields,
perform the task in bulk and predictably.

This is the point where the Mass Translate with ChatGPT module becomes a content management tool, not just a translation tool. The prompt stops being an add-on. It becomes the actual process logic.

Full prompt versions can be very extensive, but even short excerpts clearly show the level of control.

GERDA: filling in Meta Title and Meta Description without changing product content

The prompt used for GERDA is not for translation. Its purpose is narrower and therefore highly practical: fill in missing Meta Title and Meta Description fields, clear Meta Keywords, but do not touch the rest of the record.

This is a good example of a moderation operation on product data. The model is not supposed to be creative. It is supposed to be disciplined and operate only within the boundaries of the input data.

Sample prompt excerpt:

1. Fill in `Meta Title` only if it is empty, missing, or contains a null value.2. Fill in `Meta Description` only if it is empty, missing, or contains a null value.3. Clear the `Meta Keywords` field: - always set it to an empty string ''4. Do not add any information, features, parameters, promises, or technical data that are not present in the input data.

In practice, such a prompt does three important things at once:

maintains SEO quality,
does not rewrite the entire catalog,
keeps a safe, bulk workflow on JSONL.

For stores with a large number of records, this can be more important than translation into another language itself. First, the data needs to be organized, and only then localized.

Kowal: translation, localization, and controlled content expansion

The prompt prepared for Kowal shows the other end of the use case spectrum. Here, the goal is not only to change the language, but to create one layer for:

translation,
localization,
and, if necessary, controlled content expansion as well.

This matters because in technology content, linguistic correctness alone is not enough. The technical layer must also remain intact. If the model starts translating URLs, variables, class names, API endpoints, or configuration fragments, the result quickly becomes unusable.

Sample prompt excerpt:

1. Preserve all HTML tags unchanged.2. Translate only user-visible text.3. Do not change URL addresses, placeholders, file names, paths, commands, classes, methods, functions, or API endpoints.4. If the text requires expansion, expand it only based on the information present in the source text.

This is exactly the point at which a simple instruction such as translate the text stops being enough. In Magento, you need to clearly distinguish:

what is user-facing content,
what is system structure,
what may be localized,
what the model must not touch.

VITCAS: specialized technical translation instead of literal translation

VITCAS shows a third level of flexibility: translation of specialized product content where general language understanding is not enough.

The product prompt for VITCAS is built around a very specific problem: British English in the refractory, chimney, furnace, foundry, and insulation materials industry cannot be translated literally. The model must first correctly understand the term in the industry context, and only then choose the proper market equivalent for the target country.

Sample prompt excerpt:

- “stove” usually refers to a heating stove, wood-burning stove, fireplace stove, room heater, or biomass heating appliance, not a kitchen cooker or oven- “fire cement” refers to a refractory sealing, jointing, setting, or repair compound, not structural construction cement- “board” does not automatically mean gypsum board- Always interpret ambiguous terms according to the refractory, fireplace, chimney, furnace, kiln, foundry, insulation, outdoor cooking, and high-temperature materials context

This is no longer ordinary text translation. This is control over terminology interpretation. This is exactly where it becomes clear that the Mass Translate with ChatGPT module can be used wherever industry-specific moderation of meaning and glossary control are needed across multiple markets.

VITCAS: translating blog posts stored in JSON

Another prompt for VITCAS concerns blog posts from the Amasty Blog module. Here, the task once again looks different from classic product description translation, because the model works on a data structure rather than just a single description field.

Sample prompt excerpt:

1. Return only valid JSON.2. Preserve exactly the same JSON keys.3. Translate only user-facing text values.4. Do not translate technical fields, identifiers, URLs, slugs, and configuration.5. Preserve all HTML tags unchanged.

This matters because in Magento, a lot of editorial data lives in JSON objects, blog modules, CMS blocks, and other structures that cannot be processed safely with an overly generic prompt. If the model is not given clear rules, it can easily alter technical fields or break the data structure.

Where `llms.txt` comes from and what the AI Feed module has to do with it

The most interesting case begins where translation has already been completed, but the content still requires editorial work. A good example is replacing internal links after article translation.

For such an operation to be safe, the model should not guess URLs. It must have a source of truth. This is exactly where llms.txt appears.

llms.txt files do not come from nowhere. They are the result of the AI feed layer, meaning a module such as Kowal AI Product Feed for OpenAI Vector Store. Such a module prepares publicly available, structured store data for AI systems:

product lists,
categories,
FAQ,
CMS and blog content,
URLs appropriate for a specific store view,
and the llms.txt manifest, which indicates where this data is available.

In practice, llms.txt works as an entry point for AI models and agents. Instead of crawling the site blindly, they can read where the proper feeds are located and which records describe products, categories, or posts in a given language. This is exactly the kind of infrastructure that makes more difficult editorial tasks possible without guesswork.

VITCAS: replacing links after article translation with `llms.txt`

The prompt prepared for VITCAS shows that Mass Translate with ChatGPT can also act as a tool for organizing finished content after translation.

In this case, the task is to:

recognize the target language of the article,
analyze internal links left over from the EN version,
read the correct data from the appropriate version of llms.txt,
replace only the href,
leave the anchor text and the entire article structure unchanged,
not guess addresses if the equivalent cannot be confirmed in the feed.

Sample prompt excerpt:

1. Change only internal links leading to the VITCAS store domain.2. If you do not find an unambiguous equivalent in the feed: - leave the original URL unchanged, - do not guess, - do not create a new address manually.3. Change only the `href` values in  links if they require replacement.

This is a very strong example because it shows something often overlooked in discussions about AI in Magento: the model can perform bulk maintenance tasks on content, not just create new descriptions. In a multilingual store, such an operation can be genuinely valuable, because after content translation, the internal linking between store views still needs to be cleaned up.