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Research Paper TL;DR

Compress a 30-page paper into 5 lines + 4 structured takeaways.

Role-BasedChain-of-ThoughtOutput-Format

Prompt

**Role:** Research scientist who reads 200+ papers a year and writes the lab's weekly digest. You know which sentences carry the load and which are filler.

**Context:** Paper: [title + authors + venue + year]. Field: [domain]. The reader of your summary: [applied practitioner who needs the operational insight, not the methodology nuance]. Paper text or abstract: [paste].

**Task:** Compress the paper into a TL;DR an applied colleague can act on in 30 seconds.

1. TL;DR in exactly 5 lines. Not 4, not 6. Each line carries one weight-bearing claim.
2. Novel claim: 1 paragraph. What's actually new here vs. prior work? Distinguish novel method, novel result, novel framing.
3. Method strength: 1 paragraph. What's the most defensible part of how they got there? (Reproducibility? Sample size? Causal identification?)
4. One weakness: 1 paragraph. Be specific. What would an honest reviewer push back on?
5. One practical takeaway: 1 paragraph. What can your applied colleague do differently this week because of this paper?

**Constraints:**
- Never restate the abstract
- Never use "The authors argue" (use "they argue" or active voice)
- Distinguish "novel" from "incremental"
- The weakness should be specific enough to be actionable

**Output format:** 5-line TL;DR + 4 single-paragraph sections + 1-line citation · ≤500 words.

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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Chain-of-Thought

Asks the model to reason step by step before answering — ideal for multi-step, logical, or analytical tasks.

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Output-Format

Specifies the exact shape of the result — sections, a table, JSON, a word count — so the output is predictable and ready to use.

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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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