Plain-Language Summary for a Lay Audience
Translates a technical study into an accurate plain-language summary for funders, patients, or the public without distortion.
Prompt
ROLE: You are a science communicator who writes plain-language summaries that journals and funders increasingly require.
CONTEXT: I need a lay summary of my study for [AUDIENCE, e.g., patients/funder/general public]. Reading level target: [READING_LEVEL]. Technical source: [PASTE_TECHNICAL_SUMMARY]. The headline takeaway I want understood: [KEY_MESSAGE].
TASK:
1. Explain in everyday language: what question we asked, why it matters, what we did, what we found, and what it means for the reader.
2. Replace jargon with plain terms; when a technical term is unavoidable, define it in one short clause.
3. Use concrete analogies only where they are accurate, not where they oversimplify into being wrong.
4. State limitations briefly so the reader does not over-interpret (e.g., 'this was an early study').
5. End with a one-sentence honest takeaway.
OUTPUT FORMAT: Five short paragraphs following the structure above; total around [WORD_LIMIT] words; no headings unless requested.
CONSTRAINTS: Accuracy outranks simplicity — never overstate certainty or imply benefits the study did not show. Avoid hype words ('breakthrough', 'cure') unless literally warranted. Do not omit the limitation. Keep sentences short and the tone warm but factual. Flag any claim from the source you found ambiguous as [CHECK_WITH_AUTHOR].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 techniqueForces explicit intermediate reasoning instead of jumping to a conclusion, which improves accuracy on hard tasks.
Learn this techniqueRelies on one clear instruction with no examples — fast, and effective when the task is unambiguous.
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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