AI Translation Post-Editing: What Nobody Tells You Until You've Burned a Client
Last year I watched a senior developer ship a localized SaaS product to Japan after running every string through GPT-4 and doing a 20-minute "sanity check." Three weeks post-launch, a native Japanese user filed a support ticket pointing out that the onboarding flow's CTA translated literally to "Please insert your email address into the hole." The model had chosen 穴 (hole/cavity) over 欄 (field/blank). Technically defensible. Catastrophically wrong. This is the gap that post-editing is supposed to close — and most AI workflows treat it like a formality rather than a discipline.
The Real Problem Isn't Accuracy, It's Confidence Miscalibration
Every developer who has shipped AI-translated content thinks the hard part is catching wrong translations. It isn't. Modern frontier models translate accurately at the sentence level 90%+ of the time across major language pairs. The hard part is that the remaining errors are distributed in a way that defeats normal review strategies.
AI translation errors cluster in specific zones: idiomatic expressions, domain-specific terminology with register ambiguity (formal vs. casual in Japanese, tu/vous in French for UI copy), numbers and units, and anything where the source text has intentional ambiguity (marketing copy, product names, taglines). These are also the zones your 20-minute reviewer skims fastest because everything looks fluent.
