When I review a multilingual product catalog, my first question is whether both languages describe the same product. Fluent wording is only one check. Product identity, dimensions, materials and package contents need to stay consistent. I use AI to propose language, while keeping missing product facts unresolved. This is my proposed workflow, not a claim about results from a completed client project.
Connect every language to one product record
I use stable product and variant identifiers rather than titles as matching keys. Names can vary by language, and similar names can belong to different variants. For each factual attribute, I want the value, unit, source reference and verification date recorded.
I separate translation from conversion and market-specific mapping. Choosing a local color name is different from converting a measurement. A familiar size label does not establish equivalence between two markets. I check the underlying size chart first. The same stable-identity principle applies to category mapping.
Route ambiguity to an explicit review queue
| Issue | Example | My next step |
|---|---|---|
| Missing evidence | Material is absent | Keep it unknown and request a source |
| Conflicting records | Two widths are supplied | Ask the product owner to resolve the record |
| Local terminology | A term fits two categories | Check the approved glossary and context |
| Unit conversion | A dimension needs another unit | Calculate separately and verify the result |
| Claim expansion | Translation promises stronger durability | Return to the scope supported by the source |
I start a controlled glossary with terms that affect purchase decisions: material, compatibility, care and package contents. Each entry includes preferred wording, interpretations to avoid and an example. A glossary change should identify the products that need another review.
Worked example: when a texture becomes a material claim
Imagine a source record that only says “cotton-like feel.” An English proposal saying “cotton fabric” changes a description of touch into a statement about composition. I would hold that proposal, verify the manufacturer record and remove the composition claim if evidence is unavailable. This is an illustrative case, not a description of a real product.
My review sheet connects the product ID, source phrase, Turkish proposal, English proposal, issue type, reviewer and decision. Fluency alone cannot pass that check. Numbers, negations and phrases such as “not included” must also preserve their meaning across the pair.
Ask for wording and unresolved questions separately
I ask for three outputs: proposed copy, the source fields used for factual claims, and unresolved questions. I do not ask the model to fill missing attributes with a plausible answer. The NIST generative AI risk profile describes confidently stated false content as a risk. I therefore evaluate factual support separately from language quality.
Automated checks can flag changed numbers or missing fields. I still want bilingual review when wording changes product suitability or an important claim. The offline evaluation guide explains a related distinction between hard constraints and preference quality.
Keep publication connected to later corrections
I start with a small review set containing similar model names, unfamiliar units, excluded accessories and incomplete records. I check whether the workflow stops when evidence is insufficient, as well as whether it produces good copy when evidence is clear.
Before release, I review product-variant identity, quantities, promises, language links and the visible description together. A corrected source fact sends every affected language back to review. I keep the source version, reviewer decision and published wording connected so that a correction has a clear scope and an earlier version can be identified.
My aim is consistent product meaning in useful local language. The workflow succeeds when reviewers can see what is known, what remains uncertain and what has changed since the last approved version.