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Step3 User

GPTClaudeGemini··1,017 copies·updated 2026-07-14
step3-user.prompt
Here is the {source_language} text to be translated:

<{source_language}_text>
{text}
</{source_language}_text>

Your goal is to produce an accurate {target_language} translation that preserves the nuances, tone, and stylistic elements of the original {source_language} text. Follow these steps:

1. Carefully read the {source_language} text and analyze the author's writing style. Pay attention to:
   - Sentence structure and length
   - Word choice and level of formality
   - Use of literary devices or figurative language
   - Rhythm and flow of the text
   - Any unique or distinctive elements of the author's voice

2. Begin the translation process:
   - Translate the text sentence by sentence, ensuring accuracy of meaning
   - Choose {target_language} words and phrases that best capture the tone and style of the original
   - Maintain similar sentence structures where possible, unless it compromises clarity in {target_language}
   - Preserve any idiomatic expressions, metaphors, or cultural references, adapting them if necessary to make sense in {target_language} while retaining their essence

3. After completing the translation, review it to ensure it reads naturally in {target_language} while still echoing the original {source_language} style.

4. Provide your {target_language} translation within <translation> tags.

5. After the translation, briefly explain (in 2-3 sentences) how you maintained the author's writing style in your translation. Include this explanation within <style_explanation> tags.

Remember, the goal is not just to convey the meaning, but to do so in a way that an {target_language} reader would have a similar experience to a {source_language} reader of the original text.

fill the variables

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{source_language}{text}{target_language}
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when to use it

Community prompt sourced from the open-source GitHub repo DavidLMS/aphra (MIT). A "Step3 User" style prompt — adapt the placeholders and specifics to your task. Imported as-is and not independently retested here, so check the output before relying on it.

tags

languagecommunitygeneral

source

DavidLMS/aphra · MIT