Create distinct headline angles whose claims remain inside the article's evidence. 제공할 내용: Article or detailed draft, Audience and purpose, Editorial constraints. 예상 결과: A categorized headline shortlist with claim checks, trade-offs, and test guidance.
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맥락 추가
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프롬프트
채워지지 않은 필드는 플레이스홀더로 표시되므로 프롬프트를 복사하고 편집할 수 있습니다
Generate accurate headline candidates grounded only in the supplied article.
Article, evidence, limitations, and uncertainty:
[article]
Audience, channel, search intent, action, and prior knowledge:
[audience]
Length, style, required terms, prohibited claims, brand rules, and time sensitivity:
[rules]
First extract the article's central claim, strongest supported specifics, important limitations, and terms a reader could misunderstand. If the draft does not support a promised result, causal statement, superlative, universal claim, urgency, number, or quotation, do not use it. Preserve modality such as may, can, reported, or in this sample. Do not manufacture novelty, certainty, fear, or direct quotes.
Produce distinct groups: direct/informational, benefit-with-boundary, question, search-oriented, and concise—only when suitable for the channel. Avoid candidates that differ only by synonyms. For each headline state its angle, exact supporting passage/fact, possible misreading, and character count. Flag date-sensitive wording. Then shortlist three options for different priorities and explain the trade-off. End with a literal claim audit and, if testing is requested, a valid test hypothesis and guardrail rather than a predicted winner.
기본적으로 비공개프롬프트 구성은 브라우저에서 로컬로 이루어집니다. 조직에서 허용하지 않는 한 기밀 정보를 AI 서비스에 입력하지 마세요.
입력에서 결과까지
적용 예시
구체적인 맥락이 이 레시피를 바로 사용할 수 있는 결과로 바꾸는 방법을 확인하세요
실제 입력
Article or detailed draft
A neighborhood library piloted reminder texts for 214 adult patrons who had opted in. Over six weeks, 61% of reserved items were collected before expiry, compared with 54% among 198 opted-in patrons during the prior six weeks. The periods were not randomized and seasonal demand differed, so the pilot cannot show that texts caused the change. Eleven patrons opted out; three reported messages arrived after collection. Staff spent about 25 minutes a day handling replies. The library will test timing before deciding whether to expand.
Audience and purpose
Local residents and library staff reading a public update. Search intent: understand the reminder-text pilot and what happens next. Desired action: read the results, not enroll. Readers know how reservations work but not research methods.
Editorial constraints
English headline at most 75 characters. Neutral public-service voice. Include either 'library' or 'reminder texts'. Do not say success, proven, increased, boosted, or caused. No clickbait or colon required.
예시 출력
Source boundary: collection was 61% versus 54% in two non-randomized periods; no causal conclusion is supported. The operational story also includes 11 opt-outs, three late messages, 25 staff-minutes/day, and a timing test before expansion.
Direct: “Library reports results from six-week reminder text pilot” (61 characters). Support: pilot scope and public results. Misreading risk: low; “reports” avoids endorsement. Benefit-with-boundary: “More reservations were collected during a library text pilot” (57). Support: 61% versus 54%; risk: readers may infer causation, so the article deck must immediately say the periods were not randomized. Question: “What did the library learn from its reminder text pilot?” (56). Support: results, opt-outs, delays, workload; risk: low but less search-specific. Search-oriented: “Library reminder text pilot: collection rates and next steps” (61). Support: both measures and planned timing test; risk: low. Concise: “Inside the library's reminder text pilot” (42). Support: full article; risk: gives no result signal.
Shortlist: use the search-oriented option for discoverability and scope clarity; the direct option for the safest public update; the question for a more approachable newsletter. Literal audit: no candidate says texts caused the seven-point difference, generalizes beyond opted-in adults, calls the pilot successful, or hides that expansion is undecided. Test hypothesis, if needed: explicit “rates and next steps” may improve qualified reads versus “reports results”; guardrail is no increase in readers answering that causation was proven.
효과가 있는 이유
1
Linking every headline to source evidence catches small wording changes that create much larger claims
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Distinct angle groups create useful choices instead of superficial synonym lists
결과 확인
Can every factual or benefit claim be located in the article with its limitation intact
Do candidates differ in framing rather than only vocabulary
Are causal, universal, numerical, urgent, and date-sensitive claims audited
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자주 묻는 질문
이 레시피를 언제 사용해야 하는지, 무엇을 제공해야 하는지, 그리고 어떤 부분에서 사람의 검토가 여전히 중요한지에 대한 실용적인 답변
What should I prepare before using “Generate multiple accurate non-exaggerated headlines for an article”?
For “Generate multiple accurate non-exaggerated headlines for an article,” prepare Article or detailed draft, Audience and purpose, and Editorial constraints. Replace placeholders only with information you can verify. If a detail is unknown, preserve that uncertainty explicitly instead of asking the model to infer it.
When is the “Generate multiple accurate non-exaggerated headlines for an article” result not ready to use?
The result is not ready if it does not yet deliver the stated outcome—A categorized headline shortlist with claim checks, trade-offs, and test guidance—from the supplied evidence, or if it relies on unresolved assumptions, missing approvals, or invented details. Use the checks as release gates: revise the source inputs or assign a named, authorized reviewer instead of polishing an unsupported output.
Which AI tools have recorded tests for “Generate multiple accurate non-exaggerated headlines for an article”?
The published test record for “Generate multiple accurate non-exaggerated headlines for an article” lists ChatGPT as of 2026-08-28. This confirms recorded runs, not guaranteed compatibility or identical results in later product versions. For another tool or version, keep every constraint visible and repeat the result checks before use.
ChatGPT
