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Antigravity Agent
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---
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id: wiki-2026-0508-sme
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title: SME (Subject Matter Expert / Small-Medium Enterprise)
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category: 10_Wiki/Topics
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status: verified
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canonical_id: self
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aliases: [Subject Matter Expert, Small-Medium Enterprise, Domain Expert]
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duplicate_of: none
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source_trust_level: A
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confidence_score: 0.9
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verification_status: applied
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tags: [sme, domain-expert, knowledge-elicitation, business]
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raw_sources: []
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last_reinforced: 2026-05-10
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github_commit: pending
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tech_stack:
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language: Python
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framework: Anthropic Claude API
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---
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# SME (Subject Matter Expert / Small-Medium Enterprise)
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## 매 한 줄
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> **"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.
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## 매 핵심
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### 매 SME = Subject Matter Expert
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- **역할**: deep domain knowledge — clinical, legal, mechanical, regulatory.
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- **AI context**: data labeling, evaluation rubric, RLHF preference, prompt engineering, RAG curation.
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- **Bottleneck**: SME time is the most expensive resource in vertical AI.
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- **Modern shift**: SME → AI trainer/auditor (rather than rule-author) via RLHF, eval design.
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### 매 SME = Small-Medium Enterprise
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- **EU 정의**: <250 staff, ≤€50M turnover or ≤€43M balance sheet.
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- **US (SBA)**: varies by NAICS, often <500 employees.
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- **AI context**: vertical SaaS 의 ICP (Ideal Customer Profile), self-serve onboarding, low-code AI.
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- **2026 trend**: AI-native SaaS 가 매 mid-market 을 enterprise-grade capability 로 leap-frog.
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### 매 응용
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1. SME (expert) — RLHF preference labeling, eval rubric authoring.
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2. SME (expert) — RAG document curation, golden Q&A creation.
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3. SME (business) — vertical SaaS targeting (legal, dental, HVAC).
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4. SME (business) — embedded finance, AI bookkeeping (Pilot, Bench).
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## 💻 패턴
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### SME knowledge elicitation interview (Claude-driven)
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```python
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import anthropic
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client = anthropic.Anthropic()
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INTERVIEW_PROMPT = """You are conducting a structured knowledge elicitation
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with a {domain} SME. Ask one question at a time. Build a decision tree of
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their reasoning. After each answer, ask "what edge cases?" and "what would
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make you change the answer?". Output progressive YAML knowledge graph."""
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resp = client.messages.create(
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model="claude-opus-4-7",
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max_tokens=2048,
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system=INTERVIEW_PROMPT.format(domain="cardiology triage"),
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messages=conversation_history,
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)
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```
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### SME-driven eval rubric (LLM-as-judge with SME calibration)
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```python
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RUBRIC = """
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Score 1-5 on each dimension. SME-provided anchors:
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- Clinical accuracy (5 = matches AHA guidelines, 1 = harmful)
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- Citation quality (5 = primary source, 1 = none/hallucinated)
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- Tone (5 = empathetic clinical, 1 = robotic or alarming)
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"""
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def sme_eval(question, answer, sme_anchors):
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prompt = f"{RUBRIC}\n\nSME examples:\n{sme_anchors}\n\nQ: {question}\nA: {answer}"
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return claude_judge(prompt) # returns scores + rationale
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```
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### Active learning loop with SME (cost-aware)
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```python
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import numpy as np
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def select_for_sme(pool_unlabeled, model, budget=20):
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# Uncertainty sampling — SME time is expensive, ask only on edge cases
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probs = model.predict_proba(pool_unlabeled)
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entropy = -np.sum(probs * np.log(probs + 1e-9), axis=1)
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top_k_idx = entropy.argsort()[-budget:]
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return pool_unlabeled[top_k_idx] # send these to SME
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```
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### Vertical SaaS for SME (multi-tenant Postgres RLS)
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```sql
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ALTER TABLE invoices ENABLE ROW LEVEL SECURITY;
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CREATE POLICY tenant_isolation ON invoices
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USING (tenant_id = current_setting('app.tenant_id')::uuid);
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-- App sets per-request:
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SET app.tenant_id = '123e4567-e89b-12d3-a456-426614174000';
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```
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### SME definition lookup (regulation-aware)
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```python
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SME_DEFINITIONS = {
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"EU": {"staff_max": 250, "turnover_max_eur_m": 50},
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"UK": {"staff_max": 250, "turnover_max_gbp_m": 36},
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"US_SBA": {"staff_max": 500}, # varies by NAICS
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"KR": {"staff_max": 300}, # 중소기업기본법
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}
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def is_sme(jurisdiction, staff, turnover_m):
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d = SME_DEFINITIONS[jurisdiction]
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return staff <= d["staff_max"] and turnover_m <= d.get("turnover_max_eur_m", 1e9)
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```
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### AI bookkeeping for SME (embedded LLM agent)
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```python
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def categorize_transaction(tx):
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resp = claude.messages.create(
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model="claude-opus-4-7",
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max_tokens=200,
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messages=[{"role": "user", "content": f"""
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Categorize for SME bookkeeping (US GAAP). Return JSON.
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Tx: {tx}
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Categories: {ALLOWED_GAAP_CATEGORIES}
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"""}],
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)
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return json.loads(resp.content[0].text)
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```
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## 매 결정 기준
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| 상황 | Approach |
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|---|---|
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| AI eval design | SME (expert) authoring rubrics, calibrating LLM judge |
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| RAG curation | SME (expert) curates golden corpus, validates retrieval |
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| Vertical SaaS GTM | Target SME (business) with self-serve, transparent pricing |
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| Regulatory SME definition | Use jurisdiction lookup (EU vs US SBA vs KR 중기법) |
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| Active learning budget | SME (expert) only on high-uncertainty samples |
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**기본값**: clarify which SME meaning per context; never assume.
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## 🔗 Graph
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- 부모: [[Business-Strategy]]
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- 변형: [[Domain-Expert]] · [[Startup]]
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- 응용: [[RLHF]] · [[Active Learning]]
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- Adjacent: [[LLM-as-Judge]] · [[RAG]] · [[SaaS]]
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## 🤖 LLM 활용
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**언제**: SME interview structuring, knowledge graph extraction, eval rubric drafting, SME-time amplification (ask 100 questions LLM-first, escalate to human only on disagreement).
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**언제 X**: replacing SME entirely in regulated domains (medicine, law, finance) — LLM amplifies, never substitutes liability.
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## ❌ 안티패턴
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- **Acronym ambiguity**: "let's interview SMEs" in mixed audience → confusion (experts vs companies).
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- **SME burnout**: dumping all labeling on one SME without active sampling.
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- **No SME in AI loop**: ML team builds without domain validation → ship plausible-but-wrong.
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- **Mass-market UX for SME (business)**: enterprise-style sales cycle kills SME conversion.
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## 🧪 검증 / 중복
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- Verified (EU SME definition 2003/361/EC, US SBA size standards, AIMA RLHF chapter).
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- 신뢰도 A.
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## 🕓 Changelog
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| 날짜 | 변경 |
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| 2026-05-08 | Phase 1 |
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| 2026-05-10 | Manual cleanup — dual SME meanings, knowledge elicitation, vertical SaaS |
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