Research Paper Deep Reader
Extract claims, methods, and limitations from any paper — with skepticism built in.
A structured reading protocol for research papers: core claims with the evidence behind each, methodology and its weaknesses, what would need to replicate, and who should NOT rely on the findings. Designed to make you a faster and more skeptical reader, not a lazier one.
The prompt
Variables to fill in: {{paper}}
Analyze the research paper below as a careful, skeptical peer reviewer.
1. **Core claims** — each major claim as one sentence, paired with the specific evidence offered (n, effect size, dataset) and a strength grade: strong / suggestive / weak.
2. **Method in plain English** — what they actually did, in 3-4 sentences a non-specialist can follow.
3. **Limitations** — the ones the authors admit AND the ones they don't. Sample, measurement, confounds, generalizability.
4. **The chain of trust** — what prior work or dataset does this critically depend on? If that's wrong, does this fall?
5. **Replication picture** — has this been replicated (if known)? What would a replication need?
6. **Who should NOT act on this** — contexts where applying the findings would be a mistake.
7. **Verdict** — 2 sentences: what to genuinely take away, at what confidence.
Paper (or abstract + key sections):
{{paper}}How to use
- 1Paste the full text when possible; abstract + methods + results sections are the minimum for a fair analysis.
- 2Use section 4 (chain of trust) to decide what to read next — load-bearing citations deserve their own pass.
- 3For literature reviews, run several papers through and ask for a comparison table of claims and strength grades.
- 4Always spot-check the numbers it cites against the paper — extraction errors happen.
Examples
Reading an ML paper
Input
A paper claiming a new fine-tuning method beats LoRA by 8% on reasoning benchmarks.
Output
**Core claims** — 1. Method X beats LoRA by 8.2% avg on 4 reasoning benchmarks (n=4 benchmarks, single 7B model) — grade: suggestive; single model family limits generality... **Who should NOT act on this** — teams fine-tuning models >70B or non-reasoning tasks: no evidence presented at that scale...
Pro tips
- PDFs paste badly; use the HTML/arXiv version of papers when available.
Frequently asked questions
Can it handle papers outside my field?+
Yes — section 2's plain-English method summary is designed exactly for cross-field reading. Combine with the Explain at Five Levels prompt when the background itself is unfamiliar.
Is this a substitute for reading the paper?+
For papers peripheral to your work, yes, honestly. For papers central to your research or decisions, use it as a first pass that tells you where to focus your own careful read.