GitHub Copilot Prompt for Translating a Text
GitHub Copilot, the AI development assistant created by GitHub and OpenAI, is not limited to code generation. Integrated directly into your editor (VS Code, JetBrains, Neovim), it can also serve as a powerful translation tool for your multilingual projects. Whether you need to translate localization files (i18n), code comments, technical documentation, or user interfaces, Copilot understands your project context and produces translations consistent with your existing terminology. Its main advantage lies in its ability to maintain source code formatting intact — HTML tags, interpolation variables, JSON keys — while translating only the textual content. This editor-integrated approach avoids back-and-forth with external tools and ensures your translations respect your codebase's technical conventions. Here is an optimized prompt to fully leverage GitHub Copilot in your translation tasks.
Paste in your AI
Paste this prompt in ChatGPT, Claude or Gemini and customize the variables in brackets.
Translate the following text from [SOURCE_LANGUAGE] to [TARGET_LANGUAGE]. Keep exactly the same formatting, structure, and all technical tags (HTML, variables in curly braces, JSON keys, Markdown markers). Do not translate proper names, universal technical terms, or code identifiers. Adapt idiomatic expressions to the cultural context of the target language rather than translating them literally. Maintain a consistent language register [formal/informal] throughout the text. If a term has multiple possible translations, prefer the one most common in the [FIELD: technology/marketing/legal/medical] domain. Here is the text to translate:
[PASTE_TEXT_HERE]
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Why this prompt works
This prompt works effectively because it sets explicit constraints on what should and should not be translated, eliminating common machine translation errors on code. Specifying the language register and domain allows Copilot to select the most appropriate vocabulary from its multilingual training data. Finally, the request for cultural adaptation rather than literal translation pushes the model toward professional-quality localization.
Use Cases
Variants
Expected Output
You will get a translation faithful to the original meaning, with a natural style in the target language and perfectly preserved technical formatting. Variables, tags, and code identifiers will remain intact, ready to be integrated directly into your project without manual formatting adjustments.
Frequently Asked Questions
Is GitHub Copilot reliable for translating technical content in a software project?
GitHub Copilot delivers good-quality translations for common technical content, thanks to being trained on millions of multilingual repositories. It particularly excels with localization files (JSON, YAML, .properties) and technical documentation. However, for legal, medical, or regulated content, a human review remains essential. Its main strength is its understanding of code context: it can distinguish what should be translated from what must remain untouched.
How can I translate a complete localization file (JSON/YAML) with Copilot without breaking the structure?
The most effective method is to open your source file in the editor and use the Copilot Chat with a prompt that explicitly instructs it to preserve the JSON or YAML structure. You can also reference the file directly using @workspace. Copilot will maintain the keys, indentation, and escape characters. For large files, work in sections of 50 to 100 lines to avoid truncation and keep quality high.
Which languages does GitHub Copilot handle best for translation?
Copilot performs best with languages that are widely represented in GitHub repositories: English, French, Spanish, German, Portuguese, Chinese, Japanese, and Korean. Results are also solid for Dutch, Italian, Russian, and Polish. For languages less represented in the training data, quality can be inconsistent, and comparing with a dedicated translation tool is recommended. In all cases, explicitly specifying the language pair in your prompt significantly improves accuracy.
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