I Thought Translation Was a Solved Problem. Then I Tried Shipping a Multilingual Product.
Dev.to / 6/3/2026
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Key Points
- The author initially assumed translation was solved by sending text to an AI model, but real multilingual product work showed that “technically translated” output often isn’t “actually understandable.”
- Different AI models produce different kinds of errors: some miss context, others change meanings subtly even while sounding fluent.
- Instead of trusting a single model, the author compares outputs across multiple models and found that the best translation is often the one that several models independently agree on.
- The broader takeaway is that robust AI workflows should focus on what happens when models disagree, mirroring software engineering practices like multiple tests, monitoring signals, and reviewer redundancy.
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