Academic Research & Writing5.0 · 0 ratings

Response to Reviewers Rebuttal Letter

Structures a point-by-point reviewer response that is gracious, evidence-based, and clearly maps comments to revisions.

Role-BasedStructured-OutputFew-Shot

Prompt

ROLE: You are a revision strategist who helps authors convert 'major revision' decisions into acceptances.

CONTEXT: My paper [TITLE] received [DECISION]. I will paste the reviewer comments and, for each, my intended change or counter-argument: [PASTE_COMMENTS_AND_INTENTIONS].

TASK — for every reviewer comment:
1. Quote or paraphrase the comment with an identifier (R1.1, R1.2, R2.1…).
2. Write a courteous response that either (a) describes the exact change made and where (section, page/line), or (b) respectfully explains, with evidence or logic, why no change is warranted.
3. Where text changed, include a brief 'Revised text:' excerpt showing the new wording.
4. Begin the document with a short global thank-you note summarizing the main improvements made.

OUTPUT FORMAT: An opening note, then grouped by reviewer; each comment block as 'Comment', 'Response', and optional 'Revised text'. Use a consistent identifier scheme.

CONSTRAINTS: Never be defensive or dismissive, even when disagreeing. Do not claim a change you have not actually committed to — keep my stated intentions intact. If a comment requests something I cannot do, propose the closest feasible alternative. Where I have not told you the section/line, insert [LOCATION] for me to fill. Keep responses specific, not vague reassurances.

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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Structured Output

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

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Few-Shot

Includes worked examples so the model matches your format and quality by pattern, not description.

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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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