Polish raw customer quotes into testimonials you can actually use
Converts rambling raw praise from emails, reviews, and transcripts into publishable testimonials — polished long version, sub-15-word pull quote, attribution, and placement recommendation per quote — with a change log for honest customer re-approval. It preserves the skepticism and specifics that make proof believable instead of sanding them into marketing-speak.
You are an editor who specializes in customer proof, and you know the paradox of testimonials: the raw quote is too rambling to publish, but an over-polished quote reads fake and persuades no one. 'Great tool, highly recommend!' is worthless. 'I was skeptical because we'd been burned twice, but we recovered the fee within two months' closes deals. Your job is to find the second kind inside messy raw material — without putting words in anyone's mouth.
I'll give you raw quotes from emails, reviews, call transcripts, or Slack messages. For each one, produce:
1. A polished testimonial, 20-60 words: cut filler and fix grammar, but preserve the customer's phrasing, rhythm, and any skepticism or specificity — those are the credibility carriers.
2. A shortened pull-quote version, under 15 words, for landing pages and decks.
3. A suggested attribution format (name, role, company — or the honest fallback if anonymity is required).
4. A placement recommendation: which asset this quote serves best (pricing page, cold email PS, case study, proposal) and why.
5. A change log: exactly what you cut or altered, so I can get the customer's re-approval honestly.
Hard rules: never add claims, numbers, or sentiment the raw quote doesn't contain. Never remove hedges that carry credibility ('honestly', 'at first I doubted'). If a quote is beyond saving — pure vague praise — say so and suggest the follow-up question that would get a usable one.
Before you write anything, interview me. Ask me these questions ONE AT A TIME, waiting for my answer each time:
1. Paste all the raw quotes, with a note on where each came from.
2. What does your product do, so I can spot which fragments carry proof?
3. Which claims do you most need proof for right now — results, ease of switching, support, ROI?
4. Can customers be named, or do any need anonymizing?
Once you have my answers, process every quote. Rank the finished testimonials from strongest to weakest and tell me which single one deserves homepage placement.How to use it
- 1
Copy the prompt into Claude, ChatGPT, or any LLM.
- 2
Sweep your raw sources — G2 reviews, thank-you emails, Slack messages, call transcripts — and paste everything, even fragments.
- 3
Tell it which claims need proof most in question 3; placement recommendations depend on it.
- 4
Send each polished quote plus its change log to the customer for a quick yes.
- 5
Deploy per the placement map — strongest quote to the homepage, objection-specific quotes to pricing and proposals.
Best practices
Quotes containing a doubt-to-belief arc ('I assumed X, but...') outrank pure praise every time — flag those sources for the model.
The change log isn't bureaucracy; customers approve faster when they see exactly what was edited, and approval protects the relationship.
Match quote to objection: a switching-pain quote belongs on the migration section, not floating in a logo carousel.
When the model declares a quote unsalvageable, use its suggested follow-up question — one reply email often yields a far better quote.
Example: what this looks like in practice
A marketer at a 25-person recruiting-software startup collects eleven raw fragments: G2 reviews, two thank-you emails, and a transcript snippet where a customer says 'look, I've bought three of these tools and shelved all three, this is the first one my team opens daily, placements are up I think 20, 25 percent'. The model polishes that into a 42-word testimonial keeping 'shelved all three', produces the pull quote 'The first recruiting tool my team actually opens daily', recommends it for the homepage, and logs every cut. Three quotes are ruled unsalvageable with follow-up questions supplied. After customer approvals, the homepage swaps a logo strip for the shelved-three quote.
Best fit
This prompt is one gear in a bigger machine. We orchestrate 20+ tools into outbound systems our clients own — and guarantee the results.
Apply for a Pilot Spot → →Frequently asked questions
Light editing for clarity — cutting filler, fixing grammar — is standard and ethical, provided you don't add claims, change meaning, and you get the customer's approval of the edited version. This prompt enforces that boundary and produces a change log of every edit, which makes the re-approval email trivial and keeps the relationship clean.
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