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Idiomatic Code Translation Between Languages

Ports code to a target language using native idioms and libraries, not a literal line-by-line transliteration.

Role-BasedStep-by-StepStructured-Output

Prompt

ROLE: You are a polyglot engineer fluent in the idioms, standard libraries, and ecosystems of multiple languages.

CONTEXT:
- Source language: [SOURCE_LANG]
- Target language: [TARGET_LANG]
- Source code:
```
[PASTE_CODE]
```
- Target conventions to follow: [STYLE_GUIDE, PREFERRED_LIBS, ERROR_HANDLING_STYLE]

TASK:
1. Summarize what the source code does and its key behaviors and invariants.
2. Identify constructs that do NOT map 1:1 (memory management, error handling, concurrency, nullability, iterators).
3. Translate to idiomatic target-language code — use native error handling, data structures, and standard library, not a literal port.
4. Preserve observable behavior; call out any semantic differences forced by the target language.
5. Note required dependencies and any behavior that needs a test to confirm parity.

OUTPUT FORMAT:
## Behavior Summary
## Non-Trivial Mappings (table: source construct | target idiom | why)
## Translated Code (complete, idiomatic)
## Semantic Differences & Caveats

CONSTRAINTS:
- Idiomatic over literal: write code a native of the target language would write.
- Preserve behavior; explicitly flag any unavoidable difference (e.g., integer overflow, float precision, error model).
- Do not introduce dependencies when the standard library suffices.

How to use this prompt

  1. 1

    Copy the prompt above and paste it into ChatGPT, Claude, or Gemini — or open it in the visual Studio to edit each part on a canvas and run it with your own key.

  2. 2

    Replace any bracketed placeholders with your specifics. The more concrete your context and constraints, the sharper the result — see the 5-part prompt structure.

  3. 3

    Run it, then refine. Ask the model to critique and improve its own answer with self-critique prompting.

Techniques in this prompt

Role-Based

Assigns the model an expert persona so it adopts the right vocabulary, depth, and standards for the task.

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Step-by-Step

Forces explicit intermediate reasoning instead of jumping to a conclusion, which improves accuracy on hard tasks.

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Structured Output

Pins the response to a defined structure so it drops straight into your workflow.

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Build on this prompt

Open it in the visual Studio to wire it into a full workflow with your own API key — or learn the craft behind prompts like this.

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