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

---
id: wiki-2026-0508-sme
title: SME (Subject Matter Expert / Small-Medium Enterprise)
category: 10_Wiki/Topics
status: verified
canonical_id: self
aliases: [Subject Matter Expert, Small-Medium Enterprise, Domain Expert]
duplicate_of: none
source_trust_level: A
confidence_score: 0.9
verification_status: applied
tags: [sme, domain-expert, knowledge-elicitation, business]
raw_sources: []
last_reinforced: 2026-05-10
github_commit: pending
tech_stack:
  language: Python
  framework: Anthropic Claude API
---

# SME (Subject Matter Expert / Small-Medium Enterprise)

## 매 한 줄
> **"매 SME — context dependent: AI/data project 의 SME = 매 domain expert; business/economy 의 SME = 매 small-medium enterprise (typically <250 employees)"**. 매 두 의미 가 같은 acronym 으로 충돌 — 매 audience 와 surrounding context 로 disambiguate. 매 둘 다 매 modern AI initiative (knowledge capture, vertical SaaS, AI-native SME tooling) 의 중심.

## 매 핵심

### 매 SME = Subject Matter Expert
- **역할**: deep domain knowledge — clinical, legal, mechanical, regulatory.
- **AI context**: data labeling, evaluation rubric, RLHF preference, prompt engineering, RAG curation.
- **Bottleneck**: SME time is the most expensive resource in vertical AI.
- **Modern shift**: SME → AI trainer/auditor (rather than rule-author) via RLHF, eval design.

### 매 SME = Small-Medium Enterprise
- **EU 정의**: <250 staff, ≤€50M turnover or ≤€43M balance sheet.
- **US (SBA)**: varies by NAICS, often <500 employees.
- **AI context**: vertical SaaS 의 ICP (Ideal Customer Profile), self-serve onboarding, low-code AI.
- **2026 trend**: AI-native SaaS 가 매 mid-market 을 enterprise-grade capability 로 leap-frog.

### 매 응용
1. SME (expert) — RLHF preference labeling, eval rubric authoring.
2. SME (expert) — RAG document curation, golden Q&A creation.
3. SME (business) — vertical SaaS targeting (legal, dental, HVAC).
4. SME (business) — embedded finance, AI bookkeeping (Pilot, Bench).

## 💻 패턴

### SME knowledge elicitation interview (Claude-driven)
```python
import anthropic

client = anthropic.Anthropic()
INTERVIEW_PROMPT = """You are conducting a structured knowledge elicitation
with a {domain} SME. Ask one question at a time. Build a decision tree of
their reasoning. After each answer, ask "what edge cases?" and "what would
make you change the answer?". Output progressive YAML knowledge graph."""

resp = client.messages.create(
    model="claude-opus-4-7",
    max_tokens=2048,
    system=INTERVIEW_PROMPT.format(domain="cardiology triage"),
    messages=conversation_history,
)
```

### SME-driven eval rubric (LLM-as-judge with SME calibration)
```python
RUBRIC = """
Score 1-5 on each dimension. SME-provided anchors:
- Clinical accuracy (5 = matches AHA guidelines, 1 = harmful)
- Citation quality (5 = primary source, 1 = none/hallucinated)
- Tone (5 = empathetic clinical, 1 = robotic or alarming)
"""
def sme_eval(question, answer, sme_anchors):
    prompt = f"{RUBRIC}\n\nSME examples:\n{sme_anchors}\n\nQ: {question}\nA: {answer}"
    return claude_judge(prompt)  # returns scores + rationale
```

### Active learning loop with SME (cost-aware)
```python
import numpy as np

def select_for_sme(pool_unlabeled, model, budget=20):
    # Uncertainty sampling — SME time is expensive, ask only on edge cases
    probs = model.predict_proba(pool_unlabeled)
    entropy = -np.sum(probs * np.log(probs + 1e-9), axis=1)
    top_k_idx = entropy.argsort()[-budget:]
    return pool_unlabeled[top_k_idx]  # send these to SME
```

### Vertical SaaS for SME (multi-tenant Postgres RLS)
```sql
ALTER TABLE invoices ENABLE ROW LEVEL SECURITY;
CREATE POLICY tenant_isolation ON invoices
USING (tenant_id = current_setting('app.tenant_id')::uuid);

-- App sets per-request:
SET app.tenant_id = '123e4567-e89b-12d3-a456-426614174000';
```

### SME definition lookup (regulation-aware)
```python
SME_DEFINITIONS = {
    "EU": {"staff_max": 250, "turnover_max_eur_m": 50},
    "UK": {"staff_max": 250, "turnover_max_gbp_m": 36},
    "US_SBA": {"staff_max": 500},  # varies by NAICS
    "KR": {"staff_max": 300},      # 중소기업기본법
}
def is_sme(jurisdiction, staff, turnover_m):
    d = SME_DEFINITIONS[jurisdiction]
    return staff <= d["staff_max"] and turnover_m <= d.get("turnover_max_eur_m", 1e9)
```

### AI bookkeeping for SME (embedded LLM agent)
```python
def categorize_transaction(tx):
    resp = claude.messages.create(
        model="claude-opus-4-7",
        max_tokens=200,
        messages=[{"role": "user", "content": f"""
Categorize for SME bookkeeping (US GAAP). Return JSON.
Tx: {tx}
Categories: {ALLOWED_GAAP_CATEGORIES}
"""}],
    )
    return json.loads(resp.content[0].text)
```

## 매 결정 기준
| 상황 | Approach |
|---|---|
| AI eval design | SME (expert) authoring rubrics, calibrating LLM judge |
| RAG curation | SME (expert) curates golden corpus, validates retrieval |
| Vertical SaaS GTM | Target SME (business) with self-serve, transparent pricing |
| Regulatory SME definition | Use jurisdiction lookup (EU vs US SBA vs KR 중기법) |
| Active learning budget | SME (expert) only on high-uncertainty samples |

**기본값**: clarify which SME meaning per context; never assume.

## 🔗 Graph
- 부모: [[Business-Strategy]]
- 변형: [[Domain-Expert]] · [[Startup]]
- 응용: [[RLHF]] · [[Active Learning]]
- Adjacent: [[LLM-as-Judge]] · [[RAG]] · [[SaaS]]

## 🤖 LLM 활용
**언제**: SME interview structuring, knowledge graph extraction, eval rubric drafting, SME-time amplification (ask 100 questions LLM-first, escalate to human only on disagreement).
**언제 X**: replacing SME entirely in regulated domains (medicine, law, finance) — LLM amplifies, never substitutes liability.

## ❌ 안티패턴
- **Acronym ambiguity**: "let's interview SMEs" in mixed audience → confusion (experts vs companies).
- **SME burnout**: dumping all labeling on one SME without active sampling.
- **No SME in AI loop**: ML team builds without domain validation → ship plausible-but-wrong.
- **Mass-market UX for SME (business)**: enterprise-style sales cycle kills SME conversion.

## 🧪 검증 / 중복
- Verified (EU SME definition 2003/361/EC, US SBA size standards, AIMA RLHF chapter).
- 신뢰도 A.

## 🕓 Changelog
| 날짜 | 변경 |
|---|---|
| 2026-05-08 | Phase 1 |
| 2026-05-10 | Manual cleanup — dual SME meanings, knowledge elicitation, vertical SaaS |