2nd/10_Wiki/Topics/AI_and_ML/Solution.md

---
id: wiki-2026-0508-solution
title: Solution
category: 10_Wiki/Topics
status: verified
canonical_id: self
aliases: [Solutioning, Solution Design, Problem-Solution Mapping]
duplicate_of: none
source_trust_level: A
confidence_score: 0.9
verification_status: applied
tags: [methodology, design-thinking, problem-solving, solutioning]
raw_sources: []
last_reinforced: 2026-05-10
github_commit: pending
tech_stack:
  language: methodology
  framework: design-thinking
---

# Solution

## 매 한 줄
> **"매 solution 의 problem 의 inverse 의 X — 매 fit 의 search"**. 매 problem statement → constraints → option-space → trade-off → committed solution. 매 design thinking + engineering rigor 의 fusion. 매 2026 modern PRD/RFC stack 매 LLM-aided option exploration 의 기본.

## 매 핵심

### 매 problem-vs-solution 의 분리
- **Problem**: 매 user pain, business gap, technical debt. 매 solution-agnostic description.
- **Solution**: 매 specific approach 의 implement. 매 multiple options 의 enumerate.
- **Anti-pattern**: 매 "we need X" framing — 매 solution 의 jumped-to. 매 problem 의 first articulate.

### 매 5-step canonical flow
1. **Articulate**: 매 problem 의 1-sentence + 매 measurable success criterion.
2. **Constrain**: 매 budget, deadline, team, tech-stack, risk tolerance.
3. **Enumerate**: 매 3+ options. 매 do-nothing baseline 의 always include.
4. **Trade-off**: 매 each option 의 cost/risk/value 의 score.
5. **Commit + reverse-doc**: 매 chosen option 의 RFC/ADR write. 매 rejected options 의 reason 도 기록.

### 매 응용
1. Tech RFC / ADR.
2. Product PRD.
3. Customer-discovery loop.
4. LLM-aided option generation.

## 💻 패턴

### ADR template (Markdown)
```markdown
# ADR-042: Switch to event-driven order pipeline

## Status
Accepted (2026-04-12)

## Context
Sync API call chain causes 2.3s p95 latency under peak load (12k rps).
Current monolith RPC stack cannot scale beyond 18k rps without sharding.

## Decision
Adopt Kafka-based event pipeline for order lifecycle (created → paid → shipped).

## Consequences
+ p95 drops to 400ms (validated in load test).
+ Decoupled services enable independent deploys.
- Operational complexity: Kafka cluster, schema registry, DLQ.
- 6-week migration with dual-write phase.

## Alternatives considered
1. Sharded monolith — rejected: 4mo migration, no future-proof.
2. gRPC streaming — rejected: still tightly coupled.
3. Do nothing — rejected: SLO breach by Q3.
```

### Option matrix scoring
```python
# option_matrix.py
from dataclasses import dataclass

@dataclass
class Option:
    name: str
    value: int      # 1-5 (impact)
    cost: int       # 1-5 (effort)
    risk: int       # 1-5 (uncertainty)

    @property
    def score(self) -> float:
        # Weighted: value heavy, risk penalizing
        return (self.value * 2.0) - (self.cost * 0.8) - (self.risk * 1.2)

options = [
    Option("event-pipeline", value=5, cost=4, risk=3),
    Option("sharded-monolith", value=3, cost=4, risk=2),
    Option("do-nothing", value=0, cost=0, risk=5),
]
ranked = sorted(options, key=lambda o: o.score, reverse=True)
for o in ranked:
    print(f"{o.name}: {o.score:.2f}")
```

### Problem-statement template
```markdown
**Who**: Mid-market SaaS ops engineers (50-500 employee orgs).
**What**: Cannot debug Kafka consumer lag without SSH-ing into broker.
**Why-now**: Compliance requires audit trail + zero-trust env (no SSH).
**Success**: 80% of lag incidents resolvable via dashboard alone within 2026 Q3.
**Non-goal**: Replacing existing Kafka cluster.
```

### LLM-aided option enumeration
```python
# enumerate_options.py
from anthropic import Anthropic

client = Anthropic()

def enumerate(problem: str, constraints: list[str]) -> list[dict]:
    msg = client.messages.create(
        model="claude-opus-4-7",
        max_tokens=2000,
        messages=[{
            "role": "user",
            "content": f"""Problem: {problem}
Constraints: {chr(10).join(f'- {c}' for c in constraints)}

Generate 4 distinct solution options. Include 1 'do-nothing' baseline
and 1 'wildcard' creative option. For each: name, summary, est_cost (1-5), est_risk (1-5)."""
        }]
    )
    return parse_options(msg.content[0].text)
```

### Trade-off heuristic gate
```python
def should_pursue(option) -> bool:
    if option.risk >= 5:
        return False  # too uncertain, prototype first
    if option.cost > option.value:
        return False  # net-negative
    return option.score > 0
```

### Reverse-doc rejected options
```markdown
## Rejected: GraphQL federation
**Why considered**: Frontend wants typed schema, backend wants composability.
**Why rejected**: Federation gateway adds 80ms median latency.
   Team has 0 GraphQL prod experience. 4mo onboarding curve.
**Revisit when**: Latency budget grows OR team gains GraphQL expert.
```

## 매 결정 기준
| 상황 | Approach |
|---|---|
| Vague stakeholder ask | 매 problem statement 5W 의 force |
| 2+ viable options | 매 ADR + matrix scoring |
| Reversible decision | 매 ship-and-iterate (Type 2) |
| Irreversible decision | 매 deep RFC + review (Type 1) |
| Unknown unknowns | 매 spike/prototype 의 first |

**기본값**: ADR + 3+ option enumeration + reverse-doc.

## 🔗 Graph
- 부모: [[Design-Thinking]]
- 변형: [[RFC-Process]] · [[ADR]]

## 🤖 LLM 활용
**언제**: 매 option enumeration brainstorm, 매 ADR draft, 매 problem-statement refinement, 매 reverse-doc generation.
**언제 X**: 매 commit decision (human accountability), 매 stakeholder alignment (in-person needed).

## ❌ 안티패턴
- **Solution-first**: 매 "we need Kafka" 매 problem 의 articulate 없이.
- **Single option**: 매 alternatives 0개. 매 confirmation bias.
- **No baseline**: 매 do-nothing option 의 omit. 매 cost 의 hidden.
- **No reverse-doc**: 매 rejected options 의 oral history. 매 future-team 의 repeat.
- **Solution worship**: 매 framework 의 fetishize over outcomes.

## 🧪 검증 / 중복
- Verified (Lightweight ADR by Michael Nygard; Amazon "1-pager"; ThoughtWorks Tech Radar 2026).
- 신뢰도 A.

## 🕓 Changelog
| 날짜 | 변경 |
|---|---|
| 2026-05-08 | Phase 1 |
| 2026-05-10 | Manual cleanup — full content (5-step flow + ADR/option-matrix patterns) |