Idiomatic Code Translation Between Languages
Ports code to a target language using native idioms and libraries, not a literal line-by-line transliteration.
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
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
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
Run it, then refine. Ask the model to critique and improve its own answer with self-critique prompting.
Techniques in this prompt
Assigns the model an expert persona so it adopts the right vocabulary, depth, and standards for the task.
Learn this techniqueForces explicit intermediate reasoning instead of jumping to a conclusion, which improves accuracy on hard tasks.
Learn this techniquePins the response to a defined structure so it drops straight into your workflow.
Learn this techniqueRecommended models
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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