Prompting AI in Languages Other Than English

You can prompt a multilingual model in a language other than English. OpenAI’s current API help says its models are optimized for English but trained on multilingual data and can understand and generate text across languages. That does not mean performance is the same in every language or task. Studies find language effects are not uniform. Behzad, Zeldes, and Schneider tested three models on grammaticality questions about English, prompting in English, German, Korean, Russian, and Ukrainian; prompt language significantly affected results, and non-English prompts sometimes performed better for that specific task. Other multilingual studies report disparities shaped by target language, task, model, and available data. Neither finding supports a universal rule that English prompts are always better or worse. For a practical prompt, state the language of the input and the language and locale expected in the output. Keep names, technical terms, and quoted text intact when they matter. If a task involves translation, ask for alternatives or a note about ambiguity. For high-stakes legal, medical, financial, or public information, check terminology and factual claims with a qualified fluent speaker or authoritative source. When quality is important, create a small test set in the target language and compare outputs against native-speaker judgments. Watch for dialect, script, formality, code-switching, and tokenization issues. Translation through English can help with some tasks but can also introduce errors or lose culturally specific meaning. Treat language choice as an experiment to validate for the actual use case.