How non-English developers actually use AI coding tools
A Reddit thread where developers whose first language isn't English share how they talk to AI coding tools. The debate: use English, your native language, or a mix? It touches on both output quality and cost.
Most AI coding assistants were trained mostly on English text, so they tend to give more accurate answers when prompted in English. But for non-English speakers, thinking in one language and typing instructions in another adds friction and can introduce mistakes.
This thread gathers real experiences from speakers of Korean, Spanish, German, and other languages, comparing how response quality shifts when they switch languages. There's also a practical cost angle: tokens — the units AI uses to measure text — can vary in count depending on the language, meaning your monthly bill can differ based on which language you choose to write in.
Key points
- Prompting in English often produces better AI responses due to training data bias
- Using some languages can increase token count, raising costs
- Some developers use a hybrid: English for code and variable names, native language for conversation
- AI tool quality in non-English languages varies significantly by provider
- Testing your own language strategy early can save time and money
Quick term guide
- developers
- Developers are people who build software, apps, or websites.
- AI coding tool
- Software that uses AI to help write, edit, or explain code.
- prompt
- Text instructions you give to an AI tool.
- friction
- Anything that makes it harder or slower for a user to start using a product.
- tokens
- Tokens are small pieces of text that AI systems count when reading or writing.
- prompting
- Writing instructions or questions to an AI to get a response.
- responses
- An OpenAI API feature for creating and handling model answers.
- testing
- The process of checking that software does what it's supposed to do, usually by running it and looking for errors.