Your Arabic Product Copy Sounds Translated? Build a QA Loop
Use a simple glossary, review gate and claims check to make AI-generated Arabic and Turkish product copy safer and more natural.
AI can produce a full product catalogue in Arabic or Turkish quickly. It can also make a local customer hesitate: the wording feels translated, product terms change from one page to another, or a confident-sounding claim creates a compliance problem.
The fix is not to stop using AI. Build a small language quality loop around it. This gives your team consistent terminology, local review and a clear approval step before copy reaches your Shopify store, ads or customer messages.
Decide what needs human review
Not every sentence deserves the same level of attention. A practical workflow separates low-risk copy from language that can affect trust, safety or purchase decisions.
Use three review levels
Level one: formatting and basic catalogue copy. Product dimensions, colour names, material descriptions and simple feature lists can usually be checked with automated rules and spot reviews.
Level two: persuasive copy. Headlines, benefit statements, comparison points and calls to action need a native or highly proficient reviewer. A literal translation may be grammatically correct but still sound unnatural in Turkish, Modern Standard Arabic or a Gulf market context.
Level three: claims and sensitive content. Health, beauty, finance, food, children’s products and performance claims need the strictest review. Phrases such as “guaranteed results”, “risk-free” or “clinically proven” should never pass from an AI prompt directly to a live page.
For example, if you are translating 500 product descriptions, you might automate Level One, review a sample of Level Two, and send every Level Three item to a trained human reviewer. The exact split depends on your products and risk level.
Create a local terminology sheet
AI tools often translate the same concept in different ways across product pages. That creates a catalogue that feels inconsistent, especially when customers compare products side by side.
Create a shared terminology sheet before rewriting your catalogue. Keep it short enough that your team will actually use it.
Include:
- Preferred translations for product categories and materials
- Words that should remain in English, such as technical model names
- Formal or informal customer address rules
- Preferred spelling, punctuation and number formats
- Words that are prohibited or require approval
- Market differences between Türkiye, Saudi Arabia, the UAE and other target locations
Turkish needs its own decisions around suffixes, capitalisation and natural sentence order. Arabic needs decisions around Modern Standard Arabic versus a more conversational Gulf style, as well as how measurements, currencies and product variants are displayed.
Do not ask an AI tool to “sound local” without defining what local means for your business. A Turkish fashion store, a Dubai home retailer and a B2B software company will not use the same voice.
Give the AI a controlled brief
A language model performs better when it receives your terminology and constraints as part of a repeatable brief, not as an occasional instruction from an individual employee.
A useful prompt or workflow instruction should specify:
- Target market: for example, Türkiye, Saudi Arabia or the UAE.
- Language variety: Turkish, Modern Standard Arabic or an approved conversational style.
- Audience: consumer, procurement manager, parent, technician or another defined group.
- Purpose: product page, paid ad, email, support answer or social caption.
- Required facts: only use information from the approved product record.
- Forbidden behaviour: do not invent specifications, certifications, delivery promises or discounts.
- Output format: return the headline, description, bullet points and flagged uncertainties separately.
The final instruction is important. Ask the model to mark missing information instead of filling the gap creatively. A response such as [NEEDS REVIEW: warranty period] is more useful than a polished but unsupported sentence.
Keep the source product data separate from the writing instructions. This reduces the chance that a model will mix information from neighbouring products or carry an old price into a new description.
Add a claims and compliance gate
Language quality is only one part of localisation. A sentence can sound perfectly natural and still be unsuitable for publication.
Before publishing AI-generated copy, check whether it contains:
- Health, beauty or nutrition outcomes
- Absolute promises such as “best”, “永久” or “guaranteed” equivalents
- Missing or unclear prices, taxes, delivery terms or refund conditions
- Financial or investment language
- Safety, certification or environmental claims
- Comparisons with unnamed or unverifiable alternatives
- Personal data requests or sensitive customer assumptions
Regulatory requirements vary by country, sector and channel. Treat this workflow as an operational safeguard, not legal advice. When a product is regulated or a claim could materially affect a buyer’s decision, have the responsible local or legal reviewer approve the wording.
Also preserve the original source for every approved claim. A reviewer should be able to answer: Where did this sentence come from, and who approved it? This is much easier when your workflow stores the product field, generated text, reviewer decision and publication date together.
Test language in the customer journey
A product description may look acceptable in isolation and still fail when connected to the rest of the buying experience. Review the full path, not only the catalogue.
Check one complete journey in each important market:
- Ad headline to landing-page headline
- Product title to variant selector
- Product page to cart and checkout
- Confirmation email to delivery updates
- Customer question to support response
- Return or refund request to final resolution
Look for terminology changes. If a product is called one thing in the ad and another thing in checkout, the customer may wonder whether they selected the right item. Check currency, units, delivery language and formality as well.
Ask a native reviewer to assess meaning and intent, not just grammar. Useful questions include: “Would a local customer say this?”, “Does this promise sound too strong?” and “Could this phrase be misunderstood in this market?”
A practical setup checklist
Use this sequence to improve one category or landing page before expanding across your store:
- Select 20 high-traffic products in one market.
- Record approved terms, product facts and prohibited claims.
- Write one controlled AI brief for the chosen language and channel.
- Generate copy with unknown or unsupported facts clearly flagged.
- Review terminology, tone, claims, currency, units and calls to action.
- Test the complete customer journey from ad or search result to checkout.
- Save approved examples as references for future generations.
- Track recurring errors and update the terminology sheet or workflow.
Do not measure the project only by how many descriptions were generated. Track how many needed rewriting, which error types appeared repeatedly and how long approval took. These measures show whether the process is becoming safer and more efficient.
How ADMOV can help
ADMOV can help you set up an Arabic and Turkish content workflow connected to your approved product data. We can configure LLM prompts, terminology rules, structured outputs and human approval steps for Shopify product pages, ads, support content or internal catalogue operations.
The goal is not to publish more AI copy at any cost. It is to create a repeatable system that produces locally appropriate drafts, flags uncertainty and keeps your team responsible for claims and final approval.
Book a free call to review your localisation and content workflow.