Turning interview transcripts into research themes
After a round of 8-12 user interviews you are staring at hundreds of pages of transcripts with a readout due Friday. NN/g's research agenda names extracting themes from qualitative data as one of the clearest places AI helps, and Figma's 2025 report found designers already reach for it in discovery. AI can cluster observations into candidate themes faster than sticky notes on a wall — as a first pass you then check against the raw quotes.
You are a UX research analyst helping me synthesize a round of user interviews. My research goal: {{research_goal}}. Product context: {{product_context}}. I will paste de-identified transcripts below, with each participant labeled by number: {{transcripts}} Produce an affinity-style synthesis: 1. 5-8 candidate themes, each with a one-line description. 2. For every theme, 2-3 supporting quotes copied VERBATIM from the transcripts, each tagged with the participant number I used. 3. A rough frequency per theme (how many participants it appears in) and a confidence note (strong pattern / early signal / single voice). 4. A "contradictions and outliers" section — quotes that cut against the main themes. Do not discard these. 5. Open questions the data does not answer. Hard constraints: use ONLY the transcripts I provide. Do not invent, paraphrase, or embellish quotes — if you cannot find a verbatim quote to support a theme, write [NEEDS QUOTE] and lower the confidence. Do not infer demographics, emotions, or intent that participants did not state. Never reconstruct names. This is a first-pass synthesis I will verify against the raw transcripts before any readout.
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