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---
id: wiki-2026-0508-continuous-discovery
title: Continuous Discovery
category: 10_Wiki/Topics
status: verified
canonical_id: self
aliases: [continuous user research, weekly discovery, Teresa Torres method]
duplicate_of: none
source_trust_level: A
confidence_score: 0.9
verification_status: applied
tags: [product, research, discovery, ux]
raw_sources: []
last_reinforced: 2026-05-10
github_commit: pending
tech_stack:
language: process
framework: product-discovery
---
# Continuous Discovery
## 매 한 줄
> **"매 continuous discovery 의 의미: 매 매 week 의 매 customer 와 매 conversation, 매 product decision 에 매 feed"**. 매 Teresa Torres 의 *Continuous Discovery Habits* (2021) 가 매 popularize. 매 2026 modern product team 의 매 default — 매 quarterly research → 매 weekly cadence.
## 매 핵심
### 매 Torres 의 trio
- **Product manager** + **Designer** + **Engineer** 의 매 함께 discovery
- 매 1명만 매 user 와 talk → 매 telephone game
- 매 trio 함께 → 매 shared understanding
### 매 weekly cadence
- 매 week 의 매 1+ customer interview
- 매 opportunity solution tree 의 매 update
- 매 assumption test 의 매 1+ run
### 매 Opportunity Solution Tree
- **Outcome** (top): business outcome (매 retention +5%)
- **Opportunities**: 매 customer needs / pain points
- **Solutions**: 매 ideas
- **Experiments**: 매 assumption tests
### 매 응용
1. 매 PM 의 매 weekly research routine.
2. 매 roadmap prioritization 의 매 evidence base.
3. 매 PMF (product-market fit) 의 매 ongoing validation.
## 💻 패턴
### Opportunity Solution Tree 의 매 markdown
```markdown
# Outcome: Q2 의 weekly active users +20%
## Opportunity 1: 매 user 의 매 onboarding 에 confused
- Solution 1.1: 매 interactive tutorial
- Experiment: 매 prototype A/B test
- Solution 1.2: 매 sample data preload
- Experiment: 매 5 user 의 unmoderated test
## Opportunity 2: 매 power user 의 매 keyboard shortcut 의 X
- Solution 2.1: 매 cmdK palette
- Experiment: 매 beta cohort 측정
```
### Interview 의 매 story-based prompt
```
매 X 안 됨: "Would you use feature Y?" (매 hypothetical)
매 O: "Tell me about the last time you tried to <task>.
Walk me through what happened, step by step."
```
### Assumption Mapping
```
Importance
Low ─────────► High
┌──────────┬──────────┐
Evidence │ Skip │ TEST │
Low │ │ FIRST │
├──────────┼──────────┤
Evidence │ Document│ Build │
High │ │ │
└──────────┴──────────┘
```
### 매 weekly recurring 의 calendar block
```
Mon 10am-11am: 매 trio sync (review 의 last week 결과)
Wed 2pm-3pm: 매 customer interview slot 1
Thu 2pm-3pm: 매 customer interview slot 2
Fri 11am-12pm: 매 OST update + experiment plan
```
### Research Repository (Notion / Dovetail / Reduct)
```
/research
/interviews
2026-05-08-jane-doe-acme-corp.md
2026-05-09-john-smith-beta-inc.md
/insights
onboarding-confusion-pattern.md
/opportunity-solution-tree.md
```
### Continuous Discovery 의 매 metric
```python
weekly_metrics = {
"interviews_conducted": 3, # 매 target: 매 week 1-3
"assumptions_tested": 2,
"OST_updates": 1,
"trio_alignment_score": 4.5, # 매 self-reported 1-5
}
```
## 매 결정 기준
| 상황 | Approach |
|---|---|
| Early-stage startup | 매 founder-led, 매 5+ interviews/week |
| Growth-stage product | 매 trio cadence, 매 2-3/week |
| Enterprise B2B | 매 fewer (1-2/week), 매 deeper (60min) |
| 매 dev tool | 매 dogfood + community Discord/Slack |
| 매 heavily regulated | 매 IRB-style consent + 매 anonymization |
**기본값**: 매 weekly trio + 매 minimum 1 interview/week + 매 OST 의 living document.
## 🔗 Graph
- 변형: [[Continuous Delivery]] · [[Continuous Integration]]
## 🤖 LLM 활용
**언제**: 매 interview transcript 의 thematic coding, 매 OST 의 draft, 매 assumption 의 listing, 매 research synthesis.
**언제 X**: 매 actual customer conversation 의 X (매 LLM persona 의 fake user 의 dangerous). 매 sensitive PII 의 매 raw transcript.
## ❌ 안티패턴
- **Quarterly research**: 매 too slow, 매 stale by build time.
- **PM 만 single-handed**: 매 trio 의 X — 매 designer/eng 의 context loss.
- **매 leading question**: "Don't you hate when X?" → 매 yes-bias.
- **매 OST 의 set-and-forget**: 매 living document 의 X 인 dead artifact.
## 🧪 검증 / 중복
- Verified (Torres, *Continuous Discovery Habits*; Product Talk blog).
- 신뢰도 A.
## 🕓 Changelog
| 날짜 | 변경 |
|---|---|
| 2026-05-08 | Phase 1 |
| 2026-05-10 | Manual cleanup — Continuous Discovery full content |