Antigravity Agent
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10_Wiki/Topics 대규모 정리: - 오류 캡처/미완성 stub 문서 227개 제거 - 교차폴더 중복 43클러스터 병합 (63파일 → redirect) - 링크명 정규화: 깨진 링크 수정·redirect 직결·개념 매핑 ~2,400건 - 카테고리 MOC 6개 신규 생성 - Graph 섹션 미해결 related-keyword 링크 10,058건 제거 Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
180 lines
6.6 KiB
Markdown
180 lines
6.6 KiB
Markdown
---
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id: wiki-2026-0508-refinement
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title: Refinement
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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: [Iterative-Refinement, Self-Refine, Type-Refinement]
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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: [llm, iteration, types, design]
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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-sdk
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---
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# Refinement
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## 매 한 줄
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> **"매 첫 시도는 draft, 매 refinement는 product"**. Refinement는 초안을 critique → revise loop로 다듬어 quality를 끌어올리는 patterns의 family — LLM self-refine, type narrowing, design iteration 모두 매 같은 핵심 idea를 공유.
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## 매 핵심
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### 매 LLM Self-Refine (2024 Madaan et al. → 2026 mainstream)
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- **Generate → Critique → Refine** loop. Single model이 매 세 role을 모두 수행.
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- 매 효과: math, code, dialog 매 +5–20% accuracy without extra training.
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- **Reflexion** (Shinn 2023)은 verbal RL — 매 episode 끝에 매 self-critique를 episodic memory로 저장.
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- 2026 standard: Claude Opus 4.7 / GPT-5 매 native "extended thinking" mode가 매 refine을 internal로 흡수 → external loop는 매 high-stakes (legal, medical) 에서만.
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### 매 Type Refinement (TypeScript / Flow / Python typing)
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- **Narrowing**: union type 의 매 instance를 specific subtype 으로 좁힘 (`typeof`, `instanceof`, discriminated union).
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- **Refinement type**: predicate-attached type — `{x: number | x > 0}`. Liquid Haskell, F* 매 사용.
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- 2026 TS 5.x: `satisfies` operator + control-flow analysis 매 강력 — 매 manual cast 의 거의 elimination.
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### 매 Design / Spec Refinement
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- **Stepwise refinement** (Wirth 1971) — abstract spec → concrete implementation을 매 단계적으로.
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- **BDD** (Given-When-Then) 매 modern incarnation.
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- AI-aided: spec → Claude → multiple impl candidates → human picks → refine.
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### 매 응용
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1. RAG answer 의 self-refine으로 hallucination ↓.
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2. Code generation 매 compile error → refine loop.
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3. TS API 의 progressive type narrowing.
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4. Product spec 의 PM ↔ AI iterative tightening.
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## 💻 패턴
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### Self-Refine loop (Anthropic SDK)
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```python
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from anthropic import Anthropic
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client = Anthropic()
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MODEL = "claude-opus-4-7"
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def self_refine(task: str, max_iter: int = 3) -> str:
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answer = client.messages.create(
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model=MODEL, max_tokens=2048,
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messages=[{"role": "user", "content": task}],
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).content[0].text
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for i in range(max_iter):
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critique = client.messages.create(
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model=MODEL, max_tokens=1024,
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system="You are a strict critic. List concrete flaws or reply 'NO_ISSUES'.",
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messages=[{"role": "user", "content": f"Task: {task}\n\nDraft:\n{answer}"}],
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).content[0].text
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if "NO_ISSUES" in critique:
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return answer
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answer = client.messages.create(
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model=MODEL, max_tokens=2048,
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messages=[{"role": "user",
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"content": f"Task: {task}\nDraft: {answer}\nCritique: {critique}\nRevise."}],
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).content[0].text
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return answer
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```
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### Reflexion-style episodic memory
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```python
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class Reflexion:
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def __init__(self):
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self.memory: list[str] = [] # accumulated lessons
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def step(self, task: str) -> str:
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ctx = "\n".join(f"- {m}" for m in self.memory[-5:])
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attempt = llm(f"Task: {task}\nPast lessons:\n{ctx}\nAct.")
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feedback = environment(attempt)
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if not feedback.success:
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lesson = llm(f"Why did this fail? Task: {task}\nAttempt: {attempt}\nFeedback: {feedback}")
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self.memory.append(lesson)
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return attempt
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```
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### TypeScript discriminated union narrowing
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```typescript
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type Result<T> =
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| { kind: 'ok'; value: T }
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| { kind: 'err'; error: Error };
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function unwrap<T>(r: Result<T>): T {
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if (r.kind === 'err') throw r.error; // narrow → 'ok' branch
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return r.value; // typed as T, no cast
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}
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```
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### Python TypeGuard refinement
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```python
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from typing import TypeGuard
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def is_str_list(x: list[object]) -> TypeGuard[list[str]]:
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return all(isinstance(i, str) for i in x)
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def join(items: list[object]) -> str:
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if is_str_list(items):
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return ", ".join(items) # narrowed
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raise TypeError("not str list")
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```
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### Compile-error refine loop (code generation)
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```python
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def codegen_refine(spec: str, max_iter=5):
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code = llm(f"Write Python for: {spec}")
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for _ in range(max_iter):
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ok, err = run_pytest(code)
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if ok: return code
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code = llm(f"Spec: {spec}\nCode: {code}\nFailing: {err}\nFix.")
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raise RuntimeError("refinement budget exhausted")
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```
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### Best-of-N + judge (ensemble refinement)
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```python
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def best_of_n(prompt: str, n: int = 5) -> str:
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candidates = [llm(prompt, temperature=1.0) for _ in range(n)]
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ranking = llm(f"Pick best of these:\n{candidates}\nReturn index 0..{n-1}")
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return candidates[int(ranking.strip())]
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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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| Single-shot good enough (chat) | No refine — extra cost |
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| High-stakes (legal/medical) | External self-refine + human review |
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| Code with tests | Compile/test-driven refine |
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| Long agentic task | Reflexion (episodic memory) |
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| Reasoning math | extended thinking (native) — already refine internally |
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| TS API design | Narrowing + `satisfies` |
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**기본값**: native extended thinking 우선 → 부족하면 external self-refine 1–2 iter.
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## 🔗 Graph
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- 부모: [[Iteration]]
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- 변형: [[Reflexion]] · [[Self-Consistency]] · [[Best-of-N]]
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- 응용: [[RAG]] · [[Code-Generation]]
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- Adjacent: [[TypeScript 타입 시스템 (TypeScript Type System)|Type-System]] · [[Stepwise-Refinement]] · [[BDD]]
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## 🤖 LLM 활용
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**언제**: high-stakes output, agentic loops, code with verifiable feedback.
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**언제 X**: 매 latency-sensitive UX, 매 simple chat — extra latency × cost는 매 안 맞음.
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## ❌ 안티패턴
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- **Infinite refine loop**: max_iter 의 hard cap 의 X → cost explosion.
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- **Same-model critique only**: 매 critic = generator인 경우 같은 blind spot. Mix models (Opus critic, Sonnet generator).
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- **Refine without termination signal**: "NO_ISSUES" 같은 매 explicit stop 의 부재 → 매 endless tweaking.
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- **Type assertion 으로 narrow**: TS 매 `as` 사용은 매 refinement 의 X — 매 unsafe cast.
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## 🧪 검증 / 중복
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- Verified (Madaan 2024 Self-Refine, Shinn 2023 Reflexion, TS handbook narrowing).
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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 — full rewrite covering LLM self-refine, type narrowing, design iteration |
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