Dohyun Jung - Principal Consultant, ROBOCO

There is one question people commonly wonder about when it comes to vibe coding, that is, developing with a large language model (LLM) like GPT: “Is there a difference in the quality of the work when I talk to the model in Korean versus English?” To give the conclusion first, in my experience and by the way LLMs work, the language itself does not change the quality of the work.

TL;DR

  • What matters more than which language, Korean or English, produces better results is token efficiency.
  • Korean can consume more tokens to convey the same content, which can affect cost, speed, and how much context you can pass along.
  • For large projects, or for documents that get fed in repeatedly such as rules files, it is worth considering a strategy of keeping them concise and in English.

Let’s start with a quick look at the mechanics. An LLM breaks the input text into units called tokens, then uses those tokens to predict what comes next and carry out the work. In English, a word or part of a word is generally split into a single token, while Korean is split more finely, at the syllable or morpheme level.

For example, “Hello” typically uses one token, whereas “안녕하세요” is likely to be split into several tokens such as ‘안’, ‘녕’, ‘하세요’ — though this can vary depending on the internal workings of the LLM service.

That said, it is hard to argue that this affects the quality of the work. In the end, LLMs overcome differences between languages quite well in the process of understanding the input and grasping the context.

There is, however, something else worth paying attention to: token consumption. English consumes tokens relatively concisely, while Korean requires more tokens to convey the same content. This is not merely a cost issue; it also affects prompt processing speed and the total size of the context you can pass along.

Rules files used in vibe coding environments — .cursorrules (Cursor), .windsurfrules (Windsurf), CLAUDE.md (Claude Code) — are fed in repeatedly with every exchange with the model, which makes optimizing those rules extremely important. This is where the question of which language to choose takes on practical meaning. Writing your rules and documents in a more concise language cuts unnecessary token consumption and lets you pass along more context.

For most development work, using Korean does not actually cause any serious problem. But for large projects, or when you have to handle complex context, giving English a look can be a good approach. It lets you build a faster, more efficient vibe coding environment while minimizing token usage.

Ultimately, the question of which language, Korean or English, works “better” is at its core a question of efficiency rather than of quality. When cost, speed, and efficient context management are what you need, writing your prompts and rules in English is the smart choice.