---
id: wiki-2026-0508-big-picture
title: Big Picture
category: 10_Wiki/Topics
status: verified
canonical_id: self
aliases: [Big Picture Thinking, System-Level View, Holistic View]
duplicate_of: none
source_trust_level: A
confidence_score: 0.9
verification_status: applied
tags: [meta, systems-thinking, architecture, decision-making]
raw_sources: []
last_reinforced: 2026-05-10
github_commit: pending
tech_stack:
  language: python
  framework: general
---

# Big Picture

## 매 한 줄
> **"매 zoom out before you zoom in"**. Big Picture thinking 매 system-level perspective 의 prioritization — local optimization 매 global suboptimum 의 lead 가능. 2026 LLM 시대 매 context window 1M+ tokens 매 entire codebase 의 single prompt 의 fit 가능 — Big Picture 매 finally tractable computationally.

## 매 핵심

### 매 Levels of abstraction
- **L0 (atom)**: single function, single line.
- **L1 (module)**: file, class, single concern.
- **L2 (subsystem)**: service, package, bounded context.
- **L3 (system)**: full application, deployment topology.
- **L4 (ecosystem)**: organization, market, regulation.
- 매 mistake: L0 의 stuck — never L3 까지 zoom out.

### 매 When to zoom out
- 매 stuck 30+ min 의 single bug → L2 의 zoom out.
- 매 architectural decision → L3 mandatory.
- 매 hiring / team structure → L4.
- 매 PR review → L1 + L2 mix.

### 매 응용
1. Architecture review (data flow diagram).
2. Incident postmortem (5 whys → systemic cause).
3. Roadmap planning (quarter-level priorities).
4. Code review (cross-cutting concerns).

## 💻 패턴

### Pattern 1: Context map (L3 view)
```python
# Visualize bounded contexts (DDD-style)
contexts = {
    "auth": {"depends_on": [], "exposes": ["user_id", "session"]},
    "billing": {"depends_on": ["auth"], "exposes": ["invoice", "subscription"]},
    "notification": {"depends_on": ["auth", "billing"], "exposes": []},
}

def find_critical_path(contexts):
    """매 highest fan-in 의 service 의 SPOF candidate."""
    fan_in = {ctx: 0 for ctx in contexts}
    for ctx, info in contexts.items():
        for dep in info["depends_on"]:
            fan_in[dep] += 1
    return sorted(fan_in.items(), key=lambda x: -x[1])
```

### Pattern 2: Zoom-out checklist
```python
ZOOM_OUT_QUESTIONS = [
    "Who else is affected by this change?",
    "What breaks if this fails at 3am?",
    "Is this the right problem to solve right now?",
    "What does success look like in 6 months?",
    "Who owns this when I leave?",
]

def review_pr(pr_diff: str) -> list[str]:
    return [q for q in ZOOM_OUT_QUESTIONS if not answered_in(pr_diff, q)]
```

### Pattern 3: Pre-mortem (L4 thinking)
```python
def premortem(project: str) -> dict:
    """매 launch 전 의 'imagine it failed' exercise."""
    return {
        "tech_failure": "What technical assumption was wrong?",
        "market_failure": "Why did users not adopt?",
        "team_failure": "What organizational dynamic killed it?",
        "regulation": "What law/policy blocked it?",
    }
```

### Pattern 4: Dependency graph (L2 → L3)
```python
import networkx as nx

def build_dep_graph(modules: dict[str, list[str]]) -> nx.DiGraph:
    g = nx.DiGraph()
    for mod, deps in modules.items():
        for d in deps:
            g.add_edge(mod, d)
    cycles = list(nx.simple_cycles(g))
    if cycles:
        print(f"매 architecture smell: {len(cycles)} cycles detected")
    return g
```

### Pattern 5: LLM-assisted big picture (2026)
```python
from anthropic import Anthropic

client = Anthropic()

def architecture_summary(repo_dump: str) -> str:
    """매 1M context 의 entire repo 의 fit — 2026 standard."""
    msg = client.messages.create(
        model="claude-opus-4-7-1m",
        max_tokens=4000,
        messages=[{
            "role": "user",
            "content": f"""다음 repo 의 architecture 를 L3 perspective 의 summarize.
Identify: (1) bounded contexts, (2) critical path, (3) tech debt hotspots.

{repo_dump}"""
        }],
    )
    return msg.content[0].text
```

### Pattern 6: Tradeoff matrix
```python
def tradeoff_matrix(options: list[str], criteria: list[str], scores: dict) -> str:
    rows = []
    for opt in options:
        row = [opt] + [str(scores[(opt, c)]) for c in criteria]
        rows.append(" | ".join(row))
    return "\n".join(rows)

# Usage
options = ["monolith", "microservices", "modular monolith"]
criteria = ["dev_speed", "ops_cost", "scalability", "team_autonomy"]
```

## 매 결정 기준
| 상황 | Approach |
|---|---|
| Bug fix < 1h | L0/L1 만 — zoom out 의 X. |
| Recurring bug | L2 zoom out — systemic cause. |
| New feature | L2 + L3 — fit 의 architecture. |
| Postmortem | L3 + L4 mandatory. |
| Quarterly planning | L4 only. |

**기본값**: 매 task 의 start 의 30 sec 의 L3 sketch — bounded contexts, data flow, failure modes.

## 🔗 Graph
- 부모: [[Systems_Thinking|Systems-Thinking]] · [[Architecture]]
- 응용: [[Architecture-Review]] · [[Postmortem]]
- Adjacent: [[Bounded-Context]] · [[Domain-Driven-Design]]

## 🤖 LLM 활용
**언제**: Architecture review, repo onboarding, postmortem synthesis, roadmap drafting. 매 1M context 의 entire codebase 의 fit 가능 — 매 truly novel 2026 capability.
**언제 X**: Tactical bug fix (L0/L1), perf tuning of single function. 매 LLM 매 generic advice 의 emit — local context 의 lose.

## ❌ 안티패턴
- **Premature zoom-out**: 매 every bug 의 L4 의 escalate — 매 paralysis.
- **Ivory tower architecture**: L3 만 — implementation reality 의 ignore.
- **Big-picture-only PR review**: 매 nitpick 의 miss.
- **Solo big-picture**: 매 architect 매 single person — bus factor 1.

## 🧪 검증 / 중복
- Verified: Donella Meadows "Thinking in Systems" (2008), Eric Evans "DDD" (2003), Nicole Forsgren "Accelerate" (2018).
- 신뢰도 A.
- 중복: [[Systems_Thinking|Systems-Thinking]] 매 strict superset — Big Picture 매 daily-practice variant 의 framing.

## 🕓 Changelog
| 날짜 | 변경 |
|---|---|
| 2026-05-08 | Phase 1 |
| 2026-05-10 | Manual cleanup — full content with L0-L4 levels, zoom-out patterns, LLM 1M context architecture summary |
