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Use when turning raw interview notes into themes - produces themes, verbatim quotes, and decisions or hypotheses
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| name | interview-synthesis |
| description | Use when turning raw interview notes into themes - produces themes, verbatim quotes, and decisions or hypotheses |
| kind | workflow |
| audience | anyone |
| ai-tools | any |
| complexity | guided |
| time | 30-90 min per round of interviews |
| version | 1.0.0 |
| source | bundled |
Turns raw interview notes (1-N interviews) into a structured synthesis: themes that recurred, verbatim quotes that anchor each theme, and the decisions or hypotheses the synthesis supports.
A document with: research question, methods, themes (3-7), verbatim supporting quotes per theme, surprises, and the decisions or hypotheses the data supports.
This is a workflow.
Read all the notes through once without highlighting anything. Just take in the totality. This guards against latching onto the first idea.
Go through each interview and tag observations. Tags are short — 2-4 words. Examples: "Pricing confusion," "Wants integration," "Onboarding too slow."
Don't merge tags yet. Keep them granular.
Group similar tags. Aim for 3-7 themes. If you have 1-2, you're under-clustering. If you have 15+, you're over-tagging.
A theme is interesting if it appears in more than one interview AND is non-obvious.
For each theme, pull the strongest 1-2 verbatim quotes. Use the speaker's actual words. These anchor the theme and prevent paraphrasing drift.
What did you NOT expect to hear? What contradicted your hypothesis going in? Surprises are often the most valuable output.
The synthesis should support concrete next steps. Examples:
If the synthesis doesn't support any decision or hypothesis, you under-interviewed or under-tagged.
Standard structure:
Input: 8 interviews with new SaaS users about onboarding.
Output:
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Use when browsing what spells are available - bundled seeds, your personal library, or both. Filter by kind, audience, or update status.
