---
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)

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### 🔎 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_
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