Extract and sharpen the most quotable lines from a long draft
Long articles and reports contain strong ideas buried in ordinary prose—hard to surface without reading with a very specific lens. This prompt isolates the lines most worth pulling out for pull quotes, promotional copy, or social content.
You are a senior editor preparing a piece for publication and downstream promotion. Work through the draft below in these steps.
1. FIRST PASS — CANDIDATES: Read the full draft and flag every sentence or short phrase (maximum 30 words) that is:
- A crisp, standalone claim that holds up without surrounding context
- Surprising, counterintuitive, or unusually specific
- Emotionally resonant without being sentimental
List each candidate verbatim, numbered.
2. SCORE AND RANK: Score each candidate 1–3 on three criteria (1 = weak, 3 = strong):
- Standalone clarity (does it make sense without the surrounding paragraph?)
- Distinctiveness (would this line stop someone scrolling?)
- Fidelity to the article's central argument
Show the scores and total. Rank them.
3. TOP {{NUMBER_OF_QUOTES}} QUOTES: Output only the top-ranked quotes. For each, note one suggested use case (pull quote, social post, email subject line, etc.).
4. SHARPENING: For each of the top quotes, offer one optional edit that makes it more precise or more arresting—without changing the author's meaning. Show original and edited version side by side.
Draft:
{{LONG_DRAFT_TEXT}}
Note: If the draft is a data-heavy report with few opinion statements, the candidate pool will be small. In that case, Step 3 should also flag the single most citable statistic from the text. {{NUMBER_OF_QUOTES}}{{LONG_DRAFT_TEXT}}
How to use this prompt
- Copy the prompt above (Copy button on the top-right).
- Replace each
{{VAR}}with your own value. Variables:{{NUMBER_OF_QUOTES}}{{LONG_DRAFT_TEXT}}. - Paste it into one of the recommended tools below.
- Iterate: tighten constraints in the prompt if the output is generic.
Why this prompt is structured this way
The prompt is split into explicit steps because LLMs do better when the path is named, not implied. Each variable forces specificity at the input layer — vague inputs get vague outputs.
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