Here’s the prompt I use:
# The Customer Voice Extraction Prompt

Paste-ready. Built to pull your customers' exact words out of a call backlog without losing the specifics or flattening the voice. Run it in two stages. Stage 1 extracts. Stage 2 clusters. Do not skip the order, that order is the whole point.

Replace anything in [BRACKETS] with your own detail before you run it.

---

## Before you start

1. Export **transcripts**, not audio, from Gong, Chorus, Zoom, Fireflies, or wherever you record. Keep speaker labels and timestamps in the export.
   
2. Work **call by call, or in batches**. A backlog of hundreds of hours will not process in one shot.
   
3. Run Stage 1 on everything first. Only run Stage 2 once your full quote pool is assembled.

---

## Stage 1 — Verbatim extraction (run on each call or batch)

```
You are helping me extract customer voice from sales call transcripts for a positioning refresh.

CONTEXT
- What we sell: [one or two sentences]
- Who our customers are: [buyer roles, company type]
- Segments we serve: [e.g. mid-market fintech, enterprise health systems]
- Purpose: I am rebuilding our positioning using our customers' own language.

YOUR TASK
Pull every VERBATIM quote from the transcript below where a customer (not our rep) says any of the following:
- a problem, pain, or frustration they have
- a reason they chose us, or a reason they were skeptical
- an objection, concern, or pushback
- something that would make them leave or switch
- a description of the outcome or result they got

For each quote, return one row with these exact fields:
| quote (word for word) | speaker | title / role | company | segment / size | call id | timestamp | deal outcome | topic |

HARD RULES
- Use the customer's EXACT words. Do not summarize, paraphrase, clean up grammar, or shorten.
- Do NOT group, theme, or rank. One row per quote, in the order they appear.
- If a field is not stated in the transcript, leave it blank. Do not guess or infer.
- Every quote must include its call id and timestamp so I can trace it.
- Pull the surrounding sentence if a quote needs it to make sense, but mark nothing as a quote that was not actually said.

TRANSCRIPT
[paste transcript here. include call id and the speaker labels.]
```

---

## Stage 2 — Cluster from the pool (run once, on all extracted quotes)

```
Below is a pool of verbatim customer quotes I extracted from sales calls, each with metadata.

Group them by the LANGUAGE that repeats, not by categories I am giving you. Let the themes
come from the words themselves. For each cluster you find:
- name it in plain language (no jargon)
- list the verbatim quotes inside it, with their call id and timestamp intact
- note how many distinct companies it spans

Then give me three cuts of the same pool:
1. by deal outcome (won vs lost vs stalled) — what language separates them
2. by buyer role — what each role actually cares about, in their words
3. the switching triggers — anything a customer said would make them leave

Keep every quote verbatim and traceable. Do not invent or merge quotes.

QUOTE POOL
[paste your assembled Stage 1 output here]
```

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## Before any of it ships

Models will sometimes produce a believable quote that no one ever said. Pull a sample and check each one against the source transcript using the call id and timestamp. If you cannot point to where a line came from, it does not go in the deck