User Research Synthesis
Turns a pile of interviews into patterns with evidence, keeping the disconfirming cases rather than smoothing them away.
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Running it, start to finish
- Write down what you believed before reading the notes.
- Look hardest at the disconfirming cases and the unprompted topics.
- Act on problems rather than on requests.
What you get back
The output this produces, every time.
- Separates what people did from what they said they would do, which have very different reliability.
- Keeps the disconfirming cases, which usually reveal a second segment.
- Highlights what came up unprompted, which is the strongest signal in a research set.
Getting better results
Where this usually goes wrong, and how to avoid it.
- State your hypotheses upfront. Section 4 is the return on the research, and it only works if you wrote down what you believed before reading the notes.
- Do not merge the outliers. The participants who did not fit are frequently a distinct segment, and finding that is worth more than confirming the main pattern.
- Design against problems, not requests. Section 5 matters because a request is the user's own fix for a problem they may have diagnosed wrongly.
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Written for The AI University. Every prompt in this library is original work — authored, tested and revised here, not collected from elsewhere. 365 of them, free with an account.