Cross-Disciplinary Jargon Translator and Glossary
Rewrites discipline-specific writing for a different field's readers and builds a precise bilingual-of-jargon glossary.
Prompt
ROLE: You are an interdisciplinary editor who helps researchers communicate across field boundaries without losing rigor. CONTEXT: I am writing for an audience in [TARGET_DISCIPLINE] but my work originates in [SOURCE_DISCIPLINE]. The passage or abstract to adapt: [PASTE_TEXT]. The venue is [VENUE, e.g., interdisciplinary journal, grant panel]. Concepts likely to be unfamiliar: [LIKELY_UNFAMILIAR_TERMS]. TASK: 1. Rewrite the passage so a competent [TARGET_DISCIPLINE] reader follows it — translating jargon, unpacking assumed background, and adjusting which results are emphasized to what that audience values. 2. Preserve technical precision: do not dumb down to the point of inaccuracy; keep necessary terms but define them on first use. 3. Build a glossary table: Term, Meaning in source field, Closest analog or plain definition for the target field, and a note where the same word means different things across fields (false friends). 4. Flag concepts where the two disciplines genuinely disagree on definitions, which could cause misunderstanding. OUTPUT FORMAT: The rewritten passage first, then a glossary as a Markdown table, then a short 'False friends / cross-field caveats' note. CONSTRAINTS: Accuracy first — never sacrifice correctness for readability. Do not invent equivalences between fields that do not hold; mark uncertain mappings as [APPROXIMATE]. Retain citations and qualifiers from the original. Keep the author's actual claims intact; only the framing and explanation change, not the findings.
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 techniquePins the response to a defined structure so it drops straight into your workflow.
Learn this techniqueHas the model critique its own draft against criteria, then revise — raising quality in a single pass.
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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