Turn product facts into value proposition candidates
Автор: AILesson8 мин на настройкуПроверено на:ChatGPTПроверено: 2026-08-28
Быстрый ответ
Connect verified capabilities to audience outcomes without presenting assumptions as proof. Укажите: Verified product facts, Audience evidence, Use and claim constraints. Ожидаемый результат: Ranked value proposition candidates with evidence chains, caveats, variants, and validation tests.
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Ваш промпт
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Create value proposition candidates using only the supplied evidence.
Verified product facts, limits, price, availability, performance, and proof:
[facts]
Audience segment, jobs, problems, alternatives, outcomes, objections, samples, and unknowns:
[audience]
Channel, stage, tone, length, prohibited claims, legal rules, and decision criteria:
[use]
Build an evidence chain for each candidate: audience situation → problem or desired progress → relevant capability → plausible benefit → supplied proof → limitation. Label every link confirmed, inferred, or unknown. Do not convert a feature into guaranteed time, money, health, status, or performance gains unless direct evidence supports that exact claim and population. Produce meaningfully different positioning angles, not synonyms. For each include a concise headline, one-sentence explanation, proof line, qualifier, intended segment and context, likely objection, evidence strength, and claim risk. Rank by audience relevance, differentiation, evidence, clarity, and fit to the stated channel. Propose a falsifiable validation test and decision threshold for uncertain links. List excluded claims and why.
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От исходных данных к результату
Разобранный пример
Посмотрите, как конкретный контекст превращает этот рецепт в полезный результат
Реальный ввод
Verified product facts
Meeting-notes app. Records with consent; transcribes English; lets users highlight a transcript and assign a follow-up item. Internal test: median draft transcript available 90 seconds after a 30-minute recording, n=40, stable Wi-Fi. No evidence of accuracy across accents. USD 9/user/month. Zoom only. No CRM integration.
Audience evidence
Six interviews with small remote agencies: five manually copy follow-ups after client calls; three reported missed actions; four worried automated notes may be inaccurate. All six use Zoom. Sample came from current newsletter. Desired outcome: leave calls with a reviewable follow-up list, not eliminate review.
Use and claim constraints
Homepage hero for US prospects. Plain, specific, maximum 12-word headline. No claims of perfect accuracy, no 'never miss anything,' no customer quotes, no guaranteed time savings. Rank for evidence and relevance.
Пример вывода
Reviewable follow-up angle — strongest fit
Headline: “Turn Zoom conversations into a follow-up list you review”
Explanation: highlight transcript moments and assign follow-up items after an English-language Zoom call.
Proof and qualifier: in an internal stable-Wi-Fi test, the median draft transcript for 30-minute recordings was available within 90 seconds after the call; n=40. Human review remains necessary, and accent accuracy was not measured.
Evidence chain: Zoom use and manual follow-up are confirmed in six interviews; capability is verified; a more reviewable workflow is plausible but not yet behaviorally validated. Main objection: transcript accuracy. Claim risk: low to medium.
Fast draft angle
Headline: “A draft transcript shortly after your Zoom call”
This has strong internal timing evidence but weaker connection to the audience's actual job than candidate 1. Keep “draft,” the test context, and the absence of an accuracy claim.
Action-assignment angle
Headline: “Highlight the moment, assign the next action”
Capability is verified and concrete, but evidence that assignment itself matters to this segment is incomplete. Test before making it the lead.
Validation: randomly show qualified Zoom-using agency visitors candidates 1 and 2. Predeclare primary measure as the proportion starting a product walkthrough and a minimum sample; those are currently missing. Follow with five task sessions measuring whether users can create and correctly review a follow-up list. Do not claim success from clicks alone. Exclude “never miss an action,” “perfect notes,” “works with every accent,” “saves hours,” and CRM-sync claims because the supplied evidence does not support them.
Почему это работает
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Explicit evidence chains expose the unsupported leap between capability and customer outcome.
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Distinct angles and validation rules turn copy exploration into a reviewable decision.
Проверьте результат
Can every factual phrase be traced to supplied product or audience evidence?
Are inferred benefits visibly qualified rather than guaranteed?
Do candidates differ in strategic angle and include a usable validation rule?
Используйте уверенно
Часто задаваемые вопросы
Практические ответы о том, когда использовать этот рецепт, что нужно предоставить и где по-прежнему важна проверка человеком
What should I prepare before using “Turn product facts into value proposition candidates”?
For “Turn product facts into value proposition candidates,” prepare Verified product facts, Audience evidence, and Use and claim 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 “Turn product facts into value proposition candidates” result not ready to use?
The result is not ready if it does not yet deliver the stated outcome—Ranked value proposition candidates with evidence chains, caveats, variants, and validation tests—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 “Turn product facts into value proposition candidates”?
The published test record for “Turn product facts into value proposition candidates” 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.
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ChatGPT
