The 「Language Confusion」 Incident with Claude Code
Last week, the email automation bot on our Windows device sent out an unusual signal. Emails from Chinese buyers were tagged as 「English」, while Korean messages from customers were classified as 「Other」.
At first, we thought Claude Code's language detection model was weak. But after diving into the data, the real problem was elsewhere.
The Culprit Was 「Encoding」
Our non-developer team manually examined the raw email texts and discovered that some emails were stored not in UTF-8 format, but in different encoding schemes. When the bot tried to read corrupted text, it naturally couldn't identify the language.
Original email: 你好 (Chinese)
What the bot received: ä½ å¥½ (corrupted text)
Result: Classified as 「language unknown」
The Fix and Unexpected Discovery
We added one line to the Claude Code prompt. When email data arrived, it would automatically normalize the encoding.
Surprisingly, after the fix, accuracy jumped to 98%. More importantly, among the emails that had been 「misclassified」 before, we discovered a large batch of orders with genuine problems.
The bot's mistake actually revealed hidden data issues.
Conclusion: Automation Is a Learning Process
Automation run by non-developer teams doesn't need to be perfect from day one. By tracking errors and finding patterns, a single line of correction can transform an entire workflow.
With Claude Code, it's entirely possible even without a technical background.