Synthesize Interviews

Synthesize Interviews: User Research Synthesis

Turn raw interview transcripts, notes, or feedback dumps into structured insights. Produces two outputs: a synthesis report (themes + recommendations) and a standalone pain points document (problems only, no solutions).

When to Activate

• User says "synthesize interviews", "what did users say about X"
• User provides interview transcripts or notes to analyze
• User says "analyze this feedback", "research findings"
• After 3+ customer conversations (suggest proactively)

Input Modes

Accept input three ways:

1. Pasted content: Raw transcript or notes directly in conversation
2. File path: Point to a file or directory of transcripts
3. Workspace reference: "Look at the interview notes in /research"

If input is a single interview, produce per-interview notes. If 3+ interviews, produce a cross-interview synthesis.

Preferred input: interview snapshots

For multi-interview work, the best input is one Interview Snapshot per interview, produced by /pm-discovery:interview-snapshot and tagged synthesis_status: single-interview-complete. Reading structured snapshots instead of raw transcripts is what keeps quotes traceable at synthesis time.

If the user hands over raw transcripts for 3+ interviews, say so once and offer the snapshot pass first:

"I can synthesize these directly, but collapsing per-interview reading into cross-interview synthesis is where quote accuracy degrades. Want me to run /pm-discovery:interview-snapshot on each one first? It takes one pass per interview and every quote comes back verified."

Proceed with raw transcripts if they decline. Do not block.

Hallucination guard (required)

Teresa Torres documented roughly a 30% quote hallucination rate when AI synthesizes interviews. Before delivering any synthesis that contains direct quotes:

1. For every direct quote in the output, search the source transcript or snapshot for at least 60% of its words (exact match, case-insensitive).
2. If the match fails, flag the quote inline with [UNVERIFIED - edit before citing] and keep going.
3. Never silently repair a quote you cannot match. Mark it and move on.
4. State the verified count in the output footer, for example 12/13 quotes verified against source.

Process

Step 1: Extract Raw Observations

For each interview/feedback source:

1. Identify the participant (role, context, seniority if available)
2. Extract direct quotes that reveal pain, motivation, or behavior
3. Note behaviors (what they actually do, not what they say they do)
4. Flag surprises (anything that contradicts assumptions)
5. Tag source: [Interview: {participant}, {date}]

Step 2: Identify Themes

Group observations across interviews:

1. Cluster related observations into themes (3-7 themes typical)
2. For each theme, count how many participants mentioned it
3. Rank themes by: frequency (how many said it) x intensity (how strongly they felt it)
4. Distinguish between stated needs (what they say) and observed needs (what they do)

Step 3: Generate Pain Points Document

Produce a standalone document of problems only. No solutions. This prevents premature solutioning.

# Pain Points: {Research Topic}

**Status:** Draft
**Interviews:** {count}
**Date:** {date}

## Critical Pain Points (mentioned by 60%+ participants)

### 1. {Pain point title}
- **Frequency:** {n}/{total} participants
- **Intensity:** High/Medium/Low
- **Evidence:**
  - "{Direct quote}" - {participant} [Interview: {name}, {date}]
  - "{Direct quote}" - {participant}
- **Current workaround:** {how they cope today}

## Significant Pain Points (mentioned by 30-60%)

### 2. {Pain point title}
...

## Emerging Signals (mentioned by <30% but high intensity)

### 3. {Pain point title}
...

Step 4: Generate Synthesis Report

# Research Synthesis: {Topic}

**Status:** Draft
**Interviews:** {count}
**Date:** {date}
**Researcher:** {name}

## Executive Summary
{2-3 sentence summary of the most important finding}

## Methodology
- {count} interviews with {participant profile}
- Conducted {date range}
- Method: {structured/semi-structured/contextual inquiry}

## Key Themes

### Theme 1: {Title} ({n}/{total} participants)
**Summary:** {1-2 sentences}
**Evidence:**
- "{Quote}" - {participant} [Interview: {name}, {date}]
- "{Quote}" - {participant}
**Implication:** {What this means for the product}

### Theme 2: {Title}
...

## Contradictions and Tensions
- {Where participants disagreed and why it matters}

## Surprises
- {Things that challenged our assumptions} [Assumption invalidated]

## Recommendations
1. **{Action}**: {Why, based on which themes}
2. **{Action}**: {Why}
3. **{Action}**: {Why}

## Open Questions
- {What we still don't know after this research}

## Appendix: Participant Summary
| # | Role | Context | Key Quote |
|---|------|---------|-----------|
| P1 | {role} | {context} | "{quote}" |

Step 5: Cross-Reference

After synthesis:

• "Use prd to spec solutions for the top pain points"
• "Use prioritize to rank which pain points to solve first"
• "Use lenny-podcast for Teresa Torres' Opportunity Solution Tree approach"
• Offer to update context/personas.md with new insights

Rules

• Never fabricate quotes. Every quote must come from the provided input.
• Separate observation from interpretation. Label which is which.
• Pain points doc has NO solutions. This is deliberate. Solutions come later.
• Tag evidence provenance. Every claim links to a specific participant and source.
• Preserve contradictions. If participants disagree, that's data, not noise.
• Count frequency honestly. "1 out of 3 mentioned it" is not a pattern. Say so.
• Flag small sample sizes. If fewer than 5 interviews, add [Small sample: {n} interviews. Treat as directional, not conclusive.]