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Interview synthesis: turn ten transcripts into three defensible findings

You ran a week of expert interviews and have a pile of notes. Distill them into findings you can defend, with the dissent kept visible.

The prompt — copy and run it

You are a research lead synthesizing qualitative interviews into findings for a client deck.

Produce:

A) FINDINGS — the 3-5 patterns that appear across multiple interviews, each stated as a claim with the count of sources that support it and a confidence label (strong / mixed / thin).

B) EVIDENCE MAP — a table linking each finding to the specific quotes or points I pasted, so the trail is auditable.

C) DISSENT — the credible minority views that cut against each finding, kept visible rather than averaged away.

D) OPEN QUESTIONS — what the interviews did NOT answer that the analysis still needs.

Inputs: [INTERVIEW NOTES / QUOTES] · [WHO SAID WHAT (ANONYMIZED)] · [THE QUESTION WE'RE ANSWERING]

Rules: Do not invent quotes, counts, or attributions — every finding must trace to points I actually pasted, and mark thin evidence "thin". Anonymize sources; keep confidential interview data out of consumer AI tools. This synthesizes; the interpretation and the client narrative stay yours. Verify anything uncertain against the source before relying on it.

How to use this prompt

  1. Copy the full prompt above with the Copy button.
  2. Fill in your inputs. Replace each bracketed placeholder with your specifics: [INTERVIEW NOTES / QUOTES][WHO SAID WHAT (ANONYMIZED)][THE QUESTION WE'RE ANSWERING]
  3. Paste into ChatGPT, Claude, or Gemini and run. Read the reality guardrail below before you act on the output.

Why this prompt works

Synthesis usually launders a preferred conclusion by burying the dissent; requiring a source count per finding, an auditable quote-to-finding map, and a preserved dissent section keeps the analysis honest — the consultant presents patterns that trace to real evidence, with the minority view visible instead of averaged into a false consensus.

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Reality guardrail: this prompt makes the model reason from data you paste — it does not source or verify facts for you. Check every claim, keep confidential data out of consumer AI tools, and follow your employer's AI-use policy.

Frequently asked

When should I use this prompt?

You ran a week of expert interviews and have a pile of notes. Distill them into findings you can defend, with the dissent kept visible.

Why does this prompt work?

Synthesis usually launders a preferred conclusion by burying the dissent; requiring a source count per finding, an auditable quote-to-finding map, and a preserved dissent section keeps the analysis honest — the consultant presents patterns that trace to real evidence, with the minority view visible instead of averaged into a false consensus.

What mistake does this prompt help you avoid?

Conclusion-laundering in synthesis — source counts, an auditable evidence map, and a preserved dissent section keep findings traceable and honest.

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PromptSharp prompts are drafted with AI assistance and human-reviewed. They structure how a model reasons over data you provide — they do not source or verify facts for you, and you own every output. Nothing here is financial, legal, tax, or investment advice. Never paste confidential, client, or material non-public information into consumer AI tools; follow your employer's AI-use policy. © 2026 PromptSharp.