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10_Wiki/Topics/Topic_Programming/AI_and_ML/Self-verification.md

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+---
+id: wiki-2026-0508-self-verification
+title: Self-Verification
+category: 10_Wiki/Topics
+status: verified
+canonical_id: self
+aliases: [Chain of Verification, CoVe, Self-Critique, LLM Self-Check]
+duplicate_of: none
+source_trust_level: A
+confidence_score: 0.9
+verification_status: applied
+tags: [llm, reasoning, prompting, reliability]
+raw_sources: []
+last_reinforced: 2026-05-10
+github_commit: pending
+tech_stack:
+  language: python
+  framework: Anthropic SDK / OpenAI SDK
+---
+
+# Self-Verification
+
+## 매 한 줄
+> **"매 LLM 이 자기 답을 다시 점검 — generate → verify → revise"**. 매 Dhuliawala 2023 의 CoVe (Chain of Verification), self-consistency, self-refine, reflexion 가 매 family. 매 2026: reasoning model (Claude Opus 4.7 thinking, o3) 이 매 internalized self-verify, 그래도 매 explicit verify pass 가 critical accuracy 추가.
+
+## 매 핵심
+
+### 매 형태
+- **Self-consistency** (Wang 2022): 매 sample N 개 → majority vote.
+- **Chain of Verification** (CoVe): plan → baseline → verify Qs → answer Qs → final.
+- **Self-refine** (Madaan 2023): generate → critique → revise loop.
+- **Reflexion**: episodic memory of past mistakes.
+- **Constitutional / RLHF self-judge**: model 가 own output 평가.
+
+### 매 verify 가 효과적인 곳
+- Multi-hop reasoning (factual chains).
+- Math / logic (intermediate step check).
+- Code (compile, test, lint).
+- Long-form factuality (claim-by-claim).
+- Hallucination 감소.
+
+### 매 verify 가 부정확한 곳
+- Model 의 systematic bias — 같은 wrong answer.
+- Highly creative / open-ended (no ground truth).
+- 매 verify model = generator → blind spots 공유.
+
+### 매 응용
+1. Agent loop critical-path step 검증.
+2. RAG answer claim verification (cite-check).
+3. Code review pre-PR.
+4. Math homework solver.
+5. Medical / legal high-stakes Q&A.
+
+## 💻 패턴
+
+### Self-consistency
+```python
+from collections import Counter
+samples = [llm(prompt, temperature=0.8) for _ in range(7)]
+answer = Counter(extract_answer(s) for s in samples).most_common(1)[0][0]
+```
+
+### CoVe (4 steps)
+```python
+baseline = llm(f"Answer: {q}")
+verify_qs = llm(f"List 5 verification Qs for: {baseline}")
+verify_as = [llm(f"Answer concisely: {vq}") for vq in verify_qs.splitlines()]
+final = llm(f"Given verification:\n{verify_as}\nRevise: {baseline}")
+```
+
+### Self-refine loop
+```python
+draft = llm(f"Solve: {task}")
+for _ in range(3):
+    critique = llm(f"Critique:\n{draft}\nList concrete issues; 'NONE' if perfect.")
+    if "NONE" in critique[:20]:
+        break
+    draft = llm(f"Revise based on critique:\n{critique}\n\nDraft:\n{draft}")
+```
+
+### Verifier-as-different-model
+```python
+draft = anthropic_call("claude-opus-4-7", task)
+verdict = openai_call("gpt-5", f"Find errors in:\n{draft}")
+final = anthropic_call("claude-opus-4-7", f"Address:\n{verdict}\n\nDraft:\n{draft}")
+```
+
+### Code self-test loop
+```python
+code = llm(f"Write Python for: {spec}")
+for _ in range(3):
+    res = run_tests(code, spec.tests)
+    if res.passed:
+        break
+    code = llm(f"Tests failed:\n{res.report}\nFix:\n{code}")
+```
+
+### Extended thinking (Claude 2026)
+```python
+msg = anthropic.messages.create(
+    model="claude-opus-4-7",
+    thinking={"type": "enabled", "budget_tokens": 16000},
+    messages=[{"role": "user", "content": hard_problem}],
+    max_tokens=4096,
+)
+# 매 internal verify already happens within thinking
+```
+
+### RAG claim-by-claim verify
+```python
+claims = extract_claims(answer)
+for c in claims:
+    evidence = retrieve(c)
+    ok = llm(f"Is '{c}' supported by:\n{evidence}\nyes/no")
+    if "no" in ok.lower():
+        flag(c)
+```
+
+## 매 결정 기준
+| 상황 | Approach |
+|---|---|
+| Cheap, parallelizable | self-consistency |
+| Factual long-form | CoVe |
+| Iterative improvement | self-refine |
+| Code / has tests | execution-grounded |
+| Reasoning model 사용 가능 | thinking budget + light verify |
+
+**기본값**: thinking + light claim-verify (RAG case) 또는 self-consistency (3-5 samples).
+
+## 🔗 Graph
+- 변형: [[Chain-of-Thought]] · [[Self-Consistency]] · [[Reflexion]]
+- 응용: [[RAG]] · [[Code-Generation]]
+
+## 🤖 LLM 활용
+**언제**: 매 high-stakes accuracy, hallucination cost 큼. 매 budget 가 latency 보다 중요.
+**언제 X**: 매 latency-critical (chat UI). 매 task 가 verify 가능한 ground truth 없음 (open creative).
+
+## ❌ 안티패턴
+- **Self-verify infinite loop**: 매 max iter cap 필수.
+- **Same model verify same model on bias**: blind spots 공유 → cross-model verify.
+- **Verify trivial output**: 매 cost waste — gating 필요.
+- **Trust verify verdict blindly**: verify hallucinate 가능.
+
+## 🧪 검증 / 중복
+- Verified (Wang 2022 Self-Consistency, Dhuliawala 2023 CoVe, Madaan 2023 Self-Refine).
+- 신뢰도 A.
+
+## 🕓 Changelog
+| 날짜 | 변경 |
+|---|---|
+| 2026-05-08 | Phase 1 |
+| 2026-05-10 | Manual cleanup — verification family + thinking 2026 |
+
+## 🛠️ 적용 사례 (Applied in summary)
+
+<!-- CODE-GROUNDING:START -->
+### 🔎 코드베이스 근거 (자동 추출 — E:\Wiki 레포)
+**실제 구현/사용 위치:**
+- `connectai/src/features/selfReflector/selfReflectorPrompt.ts:67` — ## [Code Self-Verification — 코드 작성 시 추가 검증]
+
+_자동 생성: code_grounding.mjs · 재실행 시 갱신됨_
+<!-- CODE-GROUNDING:END -->