Who is this prompt best for?

PhD students, postdocs, and researchers who work in wet-lab or computational biology and want to understand experimental papers beyond just the abstract. It's also useful for clinicians or clinical researchers who encounter basic science papers and need to evaluate their translational relevance.

How is this different from the clinical Feynman explainer?

The clinical explainer uses a PICO framework (Population, Intervention, Comparison, Outcome) suited to epidemiological and clinical studies. This prompt instead follows the hypothesis-experiment-mechanism logic of basic research: it covers experimental models, key lab techniques, controls, mechanistic logic chains, and the in vitro-to-human translational gap — none of which fit well in PICO.

Can I use this for omics papers (RNA-seq, proteomics, etc.)?

Yes. Omics papers are explicitly within scope. The prompt will ask the AI to explain what the omics platform measures, what the analysis pipeline produces, how many biological replicates were used, and what mechanistic conclusions (if any) the data can support vs. what remains correlative.

What if the paper combines basic research with patient samples or clinical data?

For papers that span basic and translational research (e.g., cell-line experiments validated in patient-derived samples or a mouse model plus a small human cohort), this prompt is still appropriate. It includes a translational perspective section that addresses the human relevance explicitly. For purely clinical studies, use the clinical Feynman explainer.

Feynman Basic Research Paper Explainer -- medPrompt

Usage Guide

Fill in the paper title or abstract and your research field.
Click AI Run — the explainer breaks down the paper using Feynman-style plain language.
The explanation appears in the chat — ask follow-up questions or request deeper coverage of any section.

Medical Research Assistant

Fill in variables and run directly with AI

Wiki

A comprehensive Feynman-style explainer designed specifically for basic experimental research papers, including cell biology, molecular mechanisms, animal models, and omics studies.

Unlike clinical research tools that use the PICO framework, this prompt is structured around the hypothesis-experiment-mechanism logic that drives laboratory science. It walks you through the research hypothesis behind the study, the experimental model chosen (cell line, organoid, mouse, or other system), the experimental design and controls that make the data interpretable, the key techniques employed (e.g., CRISPR, RNA-seq, flow cytometry, immunofluorescence), the mechanistic logic chain connecting data to biological conclusions, and the translational perspective — what the findings may (or may not) mean for human disease.

Basic research papers often suffer from inaccessibility: they are written for specialists and assume deep familiarity with specific methods and model systems. The Feynman technique counters this by demanding that every concept be explained in plain terms, as if teaching a motivated non-specialist. This forces the AI to surface the underlying logic rather than repeat the paper's own jargon.

The mechanistic logic chain section is particularly valuable: it traces the chain of evidence from each experiment to the conclusion it supports, and flags any gaps where correlation is being presented as causation, or where results in a cell line are being extrapolated to a whole organism.

For omics papers, the prompt specifically addresses what the platform measures, how the analysis pipeline works, what the minimum biologically meaningful threshold is, and which mechanistic claims are data-supported versus speculative. For animal model papers, it highlights which aspects of the model are validated surrogates for human disease and which are known divergences.

This tool is best suited for PhD students, postdocs, and early-career researchers who want to build genuine mechanistic understanding of papers outside their immediate subfield, or for clinician-scientists who need to evaluate the translational relevance of a preclinical finding.

FAQ