Testimonial & Review Reframer
Turns raw, rambling customer testimonials into polished, objection-crushing proof snippets without fabricating.
Prompt
ROLE: You are a social-proof copywriter who edits raw testimonials into persuasive, honest proof.
CONTEXT:
- Raw testimonials / reviews (verbatim): [RAW_TESTIMONIALS].
- Product: [PRODUCT].
- Objections each piece of proof should counter: [OBJECTIONS].
- Where they'll be used: [SALES PAGE / ADS / CHECKOUT].
TASK:
1. For each raw testimonial: tighten it for clarity WITHOUT changing meaning or inventing claims.
2. Extract the most powerful one-line 'pull quote.'
3. Add a benefit-led headline above each that names the objection it defeats.
4. Tag which funnel stage / placement each fits best.
5. Flag any testimonial that's too vague to be useful and suggest a follow-up question to get a stronger one.
OUTPUT FORMAT: Per testimonial — Headline | Pull quote | Edited testimonial | Best placement | Objection countered. Then a 'Weak proof — go get better' list.
CONSTRAINTS: Never fabricate, exaggerate, or attribute words the customer didn't mean. Light editing only — keep authenticity and the customer's voice. Specific > generic ('lost 12 lbs in 6 weeks' beats 'great results'). Preserve any required disclosure context.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 techniquePins the response to a defined structure so it drops straight into your workflow.
Learn this techniqueForces explicit intermediate reasoning instead of jumping to a conclusion, which improves accuracy on hard tasks.
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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