Reformulate a Raw Client Testimonial into Impactful Content
Transforms raw client testimonials into multiple exploitable formats (short, long, social media) without distorting the content.
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Paste this prompt in ChatGPT, Claude or Gemini and customize the variables in brackets.
Reformule et structure le témoignage client brut suivant pour le rendre plus impactant, sans en dénaturer le sens ni inventer des informations.
Témoignage original brut : [COPIER-COLLER LE TÉMOIGNAGE ORIGINAL]
Nom du client : [PRÉNOM + INITIALE DU NOM]
Titre / Fonction : [TITRE PROFESSIONNEL]
Entreprise : [NOM DE L'ENTREPRISE]
Produit / Service concerné : [PRODUIT OU SERVICE]
Contexte d'utilisation : [COMMENT LE CLIENT UTILISE LE PRODUIT]
Transformations à effectuer :
- VERSION COURTE (50 mots max) : Extrait le plus percutant, chiffres en avant
- VERSION LONGUE (150-200 mots) : Restructure en Avant / Pendant / Après
- QUOTE SOCIAL MEDIA (280 caractères) : Pour Twitter/X ou LinkedIn
- FORMAT ÉTOILES : Version avec notation 5/5 et mise en forme avis Google/Trustpilot
Règles absolues :
- Ne jamais inventer de chiffres ni de faits absents du témoignage original
- Conserver la voix et les expressions distinctives du client
- Signaler par [?] les parties ambiguës qui nécessitent validation
- Suggérer une photo ou illustration adaptée à chaque format
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Answer 3 questions and Léa tailors the prompt to your situation.
Why this prompt works
The absolute rule of not inventing facts and the [?] marker for ambiguities are essential ethical safeguards. The 4 output formats allow exploiting a single testimonial across all channels, maximizing content ROI.
Use Cases
Expected Output
4 reformulated testimonial versions: short (50 words), long (150-200 words), social media format (280 characters), and star rating format, with visual suggestions.
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- LéaAI
Avant d’utiliser ce prompt, demandez au client de noter son témoignage en trois phrases : 1) contexte et problème initial, 2) solution apportée par votre produit, 3) résultat concret (chiffres si possible). Cette mini-structure en amont évite les ambiguïtés et donne au LLM une matière déjà logique pour produire des versions percutantes sans inventer de données.
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