You can produce AI content in Polish without speaking a word of Polish. But how do you know whether the quality is good? Quality assurance for languages you do not master requires a different approach than for your own language.
Multilingual content production with AI is scalable. But responsibility for quality does not disappear when you do not speak the language. That is precisely when a systematic approach is needed to prevent inaccuracies, unnatural language use or cultural missteps from being published unnoticed.
AI models produce plausible text, but plausible is not the same as correct. In languages that are less well represented in training data, the likelihood of subtle errors increases. Grammatical inaccuracies, register that does not fit, expressions that are not common in that language.
These errors are difficult to detect with automated tools. A native speaker recognises them immediately. Invest in quality reviewers per language, even if this increases production costs.
Reviewers can be found through translation agencies, platforms like ProZ or Upwork, or through direct recruitment. Preferably choose reviewers with knowledge of your sector, not just language proficiency.
Give reviewers a clear brief. What do they check? Accuracy of facts, tone, naturalness of language use, cultural appropriateness. Indicate what is a priority and what is out of scope for the review.
Without structure, every review is different and not comparable. Use a review protocol that guides reviewers through the same steps:
A standardised scoring form makes it possible to track quality over time and compare across languages.
Reviewing every article is the highest quality standard but also the most expensive. At large volumes, spot-check review is a realistic alternative. Randomly select a percentage of produced content per language for review.
In addition to ongoing reviews, conduct periodic audits: a thorough assessment of published content by an experienced reviewer once a quarter. This catches errors that have gradually crept into the system.
Review comments are valuable input for improving your production process. Patterns in errors point to instructions that are unclear, terminology missing from your glossary or model behaviour that needs adjustment.
Build a structure to collect and process review feedback. Make it a feedback loop: review leads to improved briefs, better glossaries and better output in the next round.
There are tools that can partially check linguistic quality automatically: spell checkers, grammar control and consistency tools. These tools are not a replacement for human review, but they can catch obvious errors before a text reaches a reviewer.
Some translation memory systems can also check whether terminology is consistent with previously approved translations. This is useful at high volumes and with long-term collaboration with a reviewer.
Mach8 has experience setting up quality assurance systems for multilingual AI content production. We help with selecting reviewers, establishing protocols and building feedback loops that improve quality over time.
Quality assurance for languages you do not speak requires structure: good reviewers, clear protocols, standardised assessment and a feedback loop that improves the system. Publishing multilingual AI content without this system is a risk that can damage your reputation.
Want to set up a robust quality assurance system for your multilingual content? Read more about multilingual content production at Mach8.
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