---
id: wiki-2026-0508-iterative-prompting
title: Iterative Prompting
category: 10_Wiki/Topics
status: verified
canonical_id: self
aliases: [iterative prompting, refinement, self-refine, chain-of-verification, agent loop]
duplicate_of: none
source_trust_level: A
confidence_score: 0.92
verification_status: applied
tags: [llm, prompt-engineering, iterative, self-refine, cove, agent]
raw_sources: []
last_reinforced: 2026-05-10
github_commit: pending
tech_stack:
  language: Python
  framework: LangChain / OpenAI / Anthropic
---

# Iterative Prompting

## 매 한 줄
> **"매 single prompt 의 X — 매 의 의 의 multiple round 의 의 의 quality ↑"**. 매 self-refine, CoVe (Chain-of-Verification), self-consistency, debate, agent loop. 매 modern: 매 reasoning model (o1, R1) 의 의 의 의 internal iteration. 매 trade-off: cost ↑, latency ↑.

## 매 핵심

### 매 patterns
- **Self-Refine** (Madaan 2023): 매 generate → critique → refine.
- **CoVe** (Dhuliawala 2023): 매 plan verification → check → refine.
- **Self-Consistency** (Wang 2022): 매 multiple sample → majority.
- **Debate** (Du 2023): 매 multi-LLM argue.
- **ReAct** (Yao 2022): 매 reason + act loop.
- **Reflexion** (Shinn 2023): 매 RL-style with verbal feedback.

### 매 응용
1. Math reasoning.
2. Code generation.
3. Long writing.
4. Fact verification.
5. Agent task.

## 💻 패턴

### Self-refine (Madaan)
```python
def self_refine(prompt, llm, max_iter=3):
    output = llm.generate(prompt)
    for _ in range(max_iter):
        critique = llm.generate(f"""Critique this output:
{output}
List specific issues. If perfect, say 'DONE'.""")
        if 'DONE' in critique: return output
        output = llm.generate(f"""Original: {prompt}
Previous output: {output}
Critique: {critique}
Improved output:""")
    return output
```

### Chain-of-Verification (CoVe)
```python
def cove(question, llm):
    # 매 1. Initial answer
    initial = llm.generate(f'Answer: {question}')
    
    # 매 2. Plan verification questions
    verify_qs = llm.generate(f"""Generate verification questions for:
{initial}
List as numbered questions.""").split('\n')
    
    # 매 3. Answer each independently (avoid bias)
    verifications = [llm.generate(f'Answer: {q}') for q in verify_qs if q.strip()]
    
    # 매 4. Refine
    return llm.generate(f"""Original: {initial}
Verifications: {verifications}
Refined answer accounting for any inconsistencies:""")
```

### Self-consistency
```python
def self_consistency(question, llm, n=10):
    """매 sample N times, majority vote."""
    answers = [llm.generate(question, temperature=0.7) for _ in range(n)]
    extracted = [extract_answer(a) for a in answers]
    from collections import Counter
    return Counter(extracted).most_common(1)[0][0]
```

### Multi-agent debate
```python
def debate(question, agents, n_rounds=3):
    answers = {a.name: a.generate(question) for a in agents}
    for r in range(n_rounds):
        for a in agents:
            others = '\n'.join(f'{n}: {ans}' for n, ans in answers.items() if n != a.name)
            answers[a.name] = a.generate(f"""Question: {question}
Other agents:
{others}
Refine your answer (or stick if confident):""")
    return answers
```

### ReAct (reason + act)
```python
def react(task, llm, tools):
    history = [f'Task: {task}']
    for _ in range(10):
        thought = llm.generate('\n'.join(history) + '\nThought:')
        history.append(f'Thought: {thought}')
        
        if 'final answer' in thought.lower():
            return thought
        
        action = llm.generate('\n'.join(history) + '\nAction:')
        observation = execute(action, tools)
        history.append(f'Action: {action}\nObservation: {observation}')
```

### Reflexion (verbal RL)
```python
def reflexion(task, llm, tools, max_attempts=5):
    memory = []
    for attempt in range(max_attempts):
        result = react_with_memory(task, llm, tools, memory)
        success = evaluate(result)
        if success: return result
        
        # 매 reflect
        reflection = llm.generate(f"""Task: {task}
Attempt {attempt}: {result}
What went wrong? What should I try differently?""")
        memory.append(reflection)
    return result
```

### Iterative refinement (writing)
```python
def iterative_writing(topic, llm, draft_iterations=3):
    draft = llm.generate(f'Outline: {topic}')
    
    for _ in range(draft_iterations):
        feedback = llm.generate(f"""Critique:
- Clarity (1-10)
- Argument strength (1-10)
- Specific issues
{draft}""")
        draft = llm.generate(f"""Revise based on feedback:
{feedback}
Original:
{draft}""")
    return draft
```

### Self-correction (math)
```python
def self_correct_math(problem, llm):
    answer = llm.generate(f'Solve step by step: {problem}')
    
    # 매 verify
    check = llm.generate(f"""Check this solution:
{answer}
Verify each step. If error, point it out.""")
    
    if 'error' in check.lower():
        answer = llm.generate(f"""Original: {answer}
Check: {check}
Corrected solution:""")
    
    return answer
```

### Best-of-N + judge
```python
def best_of_n(prompt, llm, judge_llm, n=8):
    candidates = [llm.generate(prompt, temperature=0.7) for _ in range(n)]
    judge_prompt = f"""Pick best answer.

Candidates:
{format_candidates(candidates)}

Output just the index."""
    best_idx = int(judge_llm.generate(judge_prompt))
    return candidates[best_idx]
```

### Tree-of-Thoughts (ToT)
```python
def tree_of_thoughts(problem, llm, branching=3, depth=4):
    """매 매 step 의 의 의 multiple thoughts → 매 best."""
    paths = [[]]
    for _ in range(depth):
        new_paths = []
        for path in paths:
            thoughts = [llm.generate(f'{problem}\nPath: {path}\nNext thought:') for _ in range(branching)]
            for t in thoughts:
                new_paths.append(path + [t])
        # 매 score + prune
        scored = [(score(p, problem, llm), p) for p in new_paths]
        paths = [p for _, p in sorted(scored, key=lambda x: -x[0])[:branching]]
    return paths[0]
```

### Cost monitoring
```python
def cost_aware_iterate(prompt, llm, budget_tokens):
    used = 0
    output = llm.generate(prompt)
    used += llm.last_usage.total_tokens
    
    while used < budget_tokens:
        critique = llm.generate(f'Critique: {output}')
        used += llm.last_usage.total_tokens
        if 'DONE' in critique or used > budget_tokens * 0.9: return output
        output = llm.generate(f'Refine: {output} {critique}')
        used += llm.last_usage.total_tokens
    return output
```

### Stop criterion (auto)
```python
def auto_stop_iterate(prompt, llm, max_iter=5):
    prev = llm.generate(prompt)
    for _ in range(max_iter):
        new = llm.generate(f'Improve: {prev}')
        if similarity(prev, new) > 0.95: return new  # 매 converged
        prev = new
    return prev
```

## 매 결정 기준
| 상황 | Pattern |
|---|---|
| Math / reasoning | Self-consistency / CoVe |
| Code | Self-refine + execute check |
| Writing | Iterative refinement |
| Open-ended | Best-of-N + judge |
| Agent task | ReAct / Reflexion |
| Complex search | Tree-of-Thoughts |

**기본값**: 매 reasoning = self-consistency (cheap) + CoVe (verify). 매 agent = ReAct + Reflexion. 매 cost-aware budget cap.

## 🔗 Graph
- 부모: [[Prompt_Engineering|Prompt-Engineering]]
- 변형: [[Self-Refine]] · [[Chain-of-Verification]] · [[ReAct]] · [[Reflexion]]
- 응용: [[Hallucination-in-LLMs]] · [[GRPO]] · [[Foundation-Models]]
- Adjacent: [[Best-of-N_Sampling]] · [[Self-Consistency]]

## 🤖 LLM 활용
**언제**: 매 reasoning. 매 high-stakes. 매 agent.
**언제 X**: 매 simple completion (cost waste).

## ❌ 안티패턴
- **No stop criterion**: 매 infinite loop.
- **No cost budget**: 매 bill shock.
- **Same prompt every iter**: 매 no progress.
- **No diverse sampling** (self-consistency): 매 same answer.
- **Skip judge**: 매 best-of-N 매 useless.

## 🧪 검증 / 중복
- Verified (Madaan Self-Refine 2023, Dhuliawala CoVe 2023, Wang Self-Consistency 2022, Yao ReAct/ToT, Shinn Reflexion 2023).
- 신뢰도 A.

## 🕓 Changelog
| 날짜 | 변경 |
|---|---|
| 2026-05-08 | Phase 1 |
| 2026-05-10 | Manual cleanup — patterns + 매 self-refine / CoVe / ReAct / Reflexion / ToT code |
