know.

CookbookWork and teamsNo. 38

A user research repositoryinterview findings you can find again

Recipe No. 38Work and teams

For
A UX researcher at a software company with two years of studies nobody can find
You bring
Your study plans, anonymised session notes, the readouts you presented, and the consent terms participants agreed to
You get
A shelf per product area, a document per study with Insight and Evidence findings, and themes you can find again with Look up across every study
Time
An evening to check each past study your assistant brings in, then an afternoon at the end of each new one
Works in
know.sh on the web · your AI assistant, through MCP
Keep it
Private. This one never goes on a public link.

A product manager asks whether anyone has heard technicians complain about working without signal. You have: in a diary study last March, in four dispatcher interviews the year before, and in a usability test nobody remembers. The quotes are in three slide decks and a folder of session notes, and it takes an afternoon to find them, so usually nobody does, and the next study asks the same question again.

This recipe keeps the research where it can be found. Each product area gets a shelf, each study a document, and each observation an Evidence finding with an anonymised quote and a session reference, while the conclusions are Insight findings that link to the evidence behind them. Findings are titled with their theme first, so a search in Look up finds a theme across every study. Your assistant does the filing, study by study, from notes you have already anonymised; you check every quote in the editor. Look up finds a quote in seconds, and the same assistant reads across studies for patterns, citing each finding it relies on.

Recordings, transcripts and the key that links participant numbers to real people stay in your research tools, under their own access controls. What comes here is anonymised before it arrives.

What you will use

Shelf
A shelf per product area, Scheduling, Invoicing and Technician app, with the current list of theme names in its notes.
Research document
A document per study; the overview gives the research question, method, participants by segment, dates and what consent covers.
Finding
Evidence for each anonymised observation, Insight for each conclusion, Question for what the study could not answer.
The A–Z index
Gathers the names and acronyms that recur (product names, GPS, SMS), with the studies they appear in.
Look up
Find every quote on a subject across studies with ⌘K, the moment someone asks.
Highlights
Highlight the strongest quote in each study; Look up lists your highlights in a group of their own.
Links between documents
Each Insight links to the Evidence findings it rests on, including evidence from earlier studies.
Your AI assistant
Files each study from your anonymised notes, then reads across every study for patterns, citing the findings it used; a local model through Ollama keeps the notes on your own machine while it works.
The editor
Where you check each quote against the notes, retitle findings to the agreed theme names and mark the insights that mattered Key.

Method

  1. 1

    Check what consent allows before anything goes in

    Read the consent terms for each study before you bring it in. They say where participants’ data may be kept, who may see it and for how long. If they, or your company’s policy on third-party tools and AI, do not cover your assistant’s provider and a library that assistant reads, keep only synthesised insights here, without quotes, or keep the study out.

    Participants appear only as numbers, P01 to P12. The key that links numbers to names and contact details stays in your research operations tool, never in know.sh.

  2. 2

    Have your assistant file each study

    Give your assistant one study’s anonymised session notes and its plan, and ask it to file a document on the right product-area shelf, titled with the method and the month: Invoice approval interviews — June 2026. The title tells a reader, and Look up, what kind of evidence is inside.

    Ask for the overview to give the research question, the method, the participants by segment (“nine office managers at companies of 20 to 200 staff”), the dates, where the recordings live, and a line on consent: “Anonymised quotes may be used internally until June 2028.”

  3. 3

    Check the Evidence findings and their quotes

    Each observation should be an Evidence finding, titled with the theme first, then the observation: “Offline work: technicians write job notes on paper and type them up at night”. Under the title comes the quote as a quotation, then a session reference line: “P07 · session 3 · 14:20”, with the link to the clip in your research tool as the source. In the editor, check every quote word for word against the notes; an assistant will tidy speech into something nobody said.

    Anonymise before your assistant sees the notes, not after. Remove names, employers, towns and anything else that could identify someone. An edited-out name stays in the finding’s earlier revisions.

  4. 4

    Write the Insights yourself

    The conclusions are your judgement, so write or rewrite each Insight yourself, even if the assistant drafted one: what you now believe, how widely you saw it (“six of nine technicians”), and what it means for the product. Link it to each Evidence finding it rests on, by pasting the address or asking your assistant to add the links, including evidence from earlier studies. An insight with one piece of evidence is a hunch; say so.

    File what the study could not answer as a Question: those are the next study’s research questions. Mark the insights that changed a product decision as Key.

  5. 5

    Let Look up become the theme map

    Keep theme names consistent: write the current list in each shelf’s notes (Offline work, End-of-day batching, Approval delays) and use them exactly. Look up (⌘K) searches every title and finding, so a theme used the same way in four studies comes back as one list.

    Search each theme every few studies. Where two names mean the same thing, “No signal” and “Offline work”, retitle the findings to one. Highlight the strongest quote in each study; the highlight shows in Look up and sets the finding’s number in bold wherever the index lists it.

  6. 6

    Answer questions with Look up

    When a product manager asks “have we heard about this?”, press ⌘K and type the word. Look up shows matching index terms, documents, findings and your highlights across every shelf, with a snippet of the text in each result. When the answer needs reading rather than finding, take the question to your assistant.

    Send the product manager the quote, the participant number and the study, never the recording link unless they already have access to it.

  7. 7

    Ask your assistant for patterns, then check every citation

    Ask your assistant to read across the whole library: where do participants describe working without signal, in which studies, and how many participants does each pattern rest on? It answers with citations to the findings it used.

    Open every cited finding. An assistant can count one participant twice, merge two different complaints into one pattern, or miss evidence phrased another way. When the pattern holds, write it up yourself as an Insight in a document called Themes across studies, linking the evidence from each study.

Specimen

Index to the library

GPSSMS

G

GPS, 4, 7, 13

Technician diary study, 4, 7

Dispatcher interviews, 13

H

Harbour, 1, 3, 5, 10

Invoice approval interviews, 3, 5

Onboarding usability test, 1, 10

Q

QuickBooks, 2, 5, 6

Invoice approval interviews, 2, 5, 6

S

SMS, 8, 10, 12

Onboarding usability test, 10

Dispatcher interviews, 8, 12

The library’s index: names and acronyms that recur across studies, each with the studies it appears in. Bold numbers are findings with a highlighted quote. Themes themselves are found with Look up.
  1. You

    Using know.sh, across all the studies in my library, where do participants describe working without signal? Group it by theme, cite each Evidence finding, and say how many participants each pattern rests on.

  2. Claude

    Three studies mention it, in two patterns.

    PatternStudiesParticipants
    Notes on paper, typed up at nightDiary study, usability test7 technicians
    Dispatch cannot see job statusDispatcher interviews3 dispatchers

    Notes on paper. Technicians in the diary study write job notes on paper when the app will not load, then type them up at home (P03, P05, P07, P09, P11). Two usability-test participants did the same.

    Job status. Dispatchers describe calling technicians to ask whether a job is finished, because the status does not update without signal.

    The diary study and the usability test may share a participant; the overviews do not say. Check before counting seven. I can draft an Insight for Themes across studies that links the findings above.

    From your library

    1. Technician diary study — March 2026, 4: Offline work: job notes on paper, typed up at night
    2. Technician diary study — March 2026, 7: Offline work: photos queued until back in the van
    3. Onboarding usability test — August 2026, 10: Offline work: app stalls at the job summary
    4. Dispatcher interviews — November 2025, 13: Offline work: dispatchers phone to check status
An assistant, here Claude, reading across the whole library. It groups what it finds, cites each finding, and flags where its own count may be wrong.

Prompts to try

  1. Your assistant, connected to know.sh (Claude, ChatGPT or a local model)

    Using know.sh, across every study in my library, where do participants describe working without signal? Group what you find by pattern, cite each Evidence finding, say how many distinct participants each pattern rests on, and flag anywhere you may be counting the same participant twice.

  2. Your assistant, connected to know.sh (Claude, ChatGPT or a local model)

    Using know.sh, read the document Invoice approval interviews — June 2026 on my Invoicing shelf and find anything that could identify a participant: a personal name, an employer, a town, a job title unusual enough to be recognised. Leave a proposed note on each passage explaining why. Do not change the text.

  3. Your assistant, connected to know.sh (Claude, ChatGPT or a local model)

    Using know.sh, list every Insight finding on the Scheduling shelf that links to fewer than two Evidence findings, and every Evidence finding that no Insight links to. Name each by document and number.

Variations

  • For stakeholders, write a separate readout document per study with the insights only, no quotes or session references, and share that one from a link if your consent terms allow it.
  • Link your Evidence findings from the requirements in a product requirements document, so each requirement shows the research behind it.
  • Doing academic research instead? A literature review applies the same evidence-and-insight discipline to papers.
  • The consent practice in an oral history collection is a good model for studies where participants may be quoted by name.

Where it falls short

  • There are no recordings or transcripts here, and no way to bring them in. You paste the anonymised excerpts; the raw data stays in your research tool.
  • There are no tags or filters by theme. Themes come from consistent titles and Look up; the index gathers only recurring names and acronyms.
  • The repository is yours alone. Designers and product managers cannot search it; they ask you, or read a document you share.
  • Your assistant’s patterns are leads. It can miss evidence phrased another way or count one participant twice, and smaller local models call tools less reliably; Revisions show everything it filed.

A note on consent, anonymisation and AI

Indexed under

Research repositoryUser interviewsAnonymisationParticipant consentResearch themesInsightsSession references