refactor(topics): 멀티 에이전트용 지식 재편 — _Common(공통 기본기) + Domain_* 구조

에이전트 8종(대화형/프로그래머 C·S/디자이너/설계자/기획자/QA/PD/PM)에게 [공통 기본 능력 + 롤별 Specialty] 2층으로 지식을 주입하기 위한 재분류. 문서 내용·포맷은 무수정, 폴더 이동만 (6,372개 문서 수 보존 확인). - Topic_Programming → Domain_Programming (내부 구조 보존) - Topic_Graphic → Domain_Design - Topic_Business → Domain_Product - Topic_General → Domain_General - _Common 신설: Math(구 Topic_Math_Specialty), Reasoning(구 General/From_Thinking & Reasoning), Reasoning_Creativity(구 General/From_창의성), Communication(Poetic_Blog_Writing + From_writing) - 타 도메인의 From_* 폴더는 유지 (출처 표기일 뿐, 이미 도메인에 맞게 분류된 문서) - 빈 폴더 정리 (memory/procedures) - 에이전트→폴더 매핑은 workspace의 .astra/agent-knowledge-map.json (9개 에이전트) Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
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+---
+id: wiki-2026-0508-dynamic-few-shot
+title: Dynamic Few-Shot Selection
+category: 10_Wiki/Topics
+status: verified
+canonical_id: self
+aliases: [dynamic few-shot, in-context learning, ICL retrieval, RAG few-shot, kNN-prompting]
+duplicate_of: none
+source_trust_level: A
+confidence_score: 0.9
+verification_status: applied
+tags: [prompt-engineering, few-shot, in-context-learning, rag, vector-search, llm, retrieval]
+raw_sources: []
+last_reinforced: 2026-05-10
+github_commit: pending
+tech_stack:
+  language: Python
+  framework: LangChain / LlamaIndex / Faiss / Chroma
+---
+
+# Dynamic Few-Shot
+
+## 매 한 줄
+> **"매 static example 의 X — 매 query-similar example 의 retrieve"**. 매 RAG-style example pool. 매 매 input 의 most relevant N example 의 inject. 매 modern: 매 hybrid (BM25 + dense) + 매 diversity rerank + 매 LLM-as-judge selection.
+
+## 매 핵심
+
+### 매 motivation
+- **Static**: 매 매 prompt 의 같은 example.
+- **Dynamic**: 매 매 query 의 best match.
+- **Result**: 매 accuracy ↑ + 매 token 의 efficient.
+
+### 매 selection strategy
+
+#### Similarity-based
+- 매 cosine on embedding.
+- 매 top-K nearest.
+
+#### Diversity (MMR)
+- 매 redundancy ↓.
+- 매 broader coverage.
+
+#### LLM-as-judge
+- 매 first retrieve N → 매 LLM 의 best K.
+- 매 expensive but high-quality.
+
+#### Skill / category-based
+- 매 query 의 type 의 classify → 매 type-specific example.
+
+#### Iterative refinement
+- 매 매 round 의 example 의 update based on output quality.
+
+### 매 retrieval method
+- **Dense** (embedding): 매 semantic similarity.
+- **BM25 / TF-IDF**: 매 keyword.
+- **Hybrid**: 매 둘 다 의 fuse.
+- **Cross-encoder rerank**: 매 expensive but accurate.
+
+### 매 응용
+1. **NER / Classification**: 매 task-type-similar example.
+2. **Code generation**: 매 similar API usage.
+3. **Translation**: 매 domain-specific phrase.
+4. **Reasoning**: 매 similar pattern (math).
+5. **Customer service**: 매 similar past issue.
+6. **Schema-aware Text2SQL**: 매 similar query pattern.
+
+### 매 modern best practice
+1. **Quality > quantity**: 매 3-5 example > 매 50.
+2. **Diverse**: 매 same domain 의 cluster X.
+3. **Recency**: 매 newer pattern.
+4. **Format consistency**: 매 same template.
+5. **Avoid leakage**: 매 test 의 example 의 X.
+
+### 매 modern AI 의 evolution
+- **In-Context Learning**: 매 GPT-3 의 zero / few-shot 의 emergence.
+- **Long context**: 매 100K+ context 의 의 매 100s example.
+- **Many-shot ICL**: 매 1000+ example (Anthropic 2024).
+- **Adaptive ICL**: 매 매 query 의 optimal length.
+
+## 💻 패턴
+
+### Basic dynamic few-shot (LangChain)
+```python
+from langchain.vectorstores import Chroma
+from langchain.embeddings import OpenAIEmbeddings
+from langchain.prompts import FewShotPromptTemplate, PromptTemplate
+
+# 매 example pool
+examples = [
+    {'question': '...', 'answer': '...'},
+    # ... 100+ examples
+]
+
+# 매 vector store
+vectordb = Chroma.from_texts(
+    [f"{e['question']} {e['answer']}" for e in examples],
+    embedding=OpenAIEmbeddings(),
+    metadatas=examples,
+)
+
+def dynamic_prompt(query, k=3):
+    relevant = vectordb.similarity_search(query, k=k)
+    selected = [doc.metadata for doc in relevant]
+    
+    example_prompt = PromptTemplate(
+        input_variables=['question', 'answer'],
+        template='Q: {question}\nA: {answer}',
+    )
+    
+    fp = FewShotPromptTemplate(
+        examples=selected,
+        example_prompt=example_prompt,
+        prefix='Answer following the format below.\n\n',
+        suffix='\n\nQ: {input}\nA:',
+        input_variables=['input'],
+    )
+    return fp.format(input=query)
+```
+
+### MMR (diversity)
+```python
+def mmr_select(query_emb, candidates, lambda_=0.7, k=5):
+    """매 Maximal Marginal Relevance — 매 relevance + 매 diversity."""
+    selected = []
+    selected_embs = []
+    
+    while candidates and len(selected) < k:
+        scores = []
+        for c in candidates:
+            relevance = cosine(query_emb, c['emb'])
+            if not selected_embs:
+                novelty = 0
+            else:
+                max_sim = max(cosine(c['emb'], se) for se in selected_embs)
+                novelty = max_sim
+            mmr = lambda_ * relevance - (1 - lambda_) * novelty
+            scores.append(mmr)
+        
+        best_idx = scores.index(max(scores))
+        selected.append(candidates[best_idx])
+        selected_embs.append(candidates[best_idx]['emb'])
+        candidates = [c for i, c in enumerate(candidates) if i != best_idx]
+    
+    return selected
+```
+
+### LLM-as-judge selection
+```python
+def llm_judge_select(query, candidates, k=5):
+    """매 first retrieve large pool → 매 LLM 의 best."""
+    # 매 1. retrieve top 20
+    pool = vectordb.similarity_search(query, k=20)
+    
+    # 매 2. LLM 의 select best 5
+    formatted = '\n\n'.join(f'[{i}] {p}' for i, p in enumerate(pool))
+    prompt = f"""Given the query: "{query}"
+
+Select the {k} MOST USEFUL examples for in-context learning.
+Consider: relevance, format, diversity, and pedagogical clarity.
+
+Examples:
+{formatted}
+
+Reply with ONLY the indices, comma-separated. e.g., 0, 3, 5, 7, 12"""
+    
+    indices = parse_indices(llm.generate(prompt))
+    return [pool[i] for i in indices]
+```
+
+### Hybrid search (BM25 + dense)
+```python
+from rank_bm25 import BM25Okapi
+import numpy as np
+
+class HybridRetriever:
+    def __init__(self, examples):
+        self.examples = examples
+        self.bm25 = BM25Okapi([e['text'].split() for e in examples])
+        self.embeddings = embed_all([e['text'] for e in examples])
+    
+    def search(self, query, k=10, alpha=0.5):
+        # 매 BM25
+        bm25_scores = self.bm25.get_scores(query.split())
+        bm25_norm = bm25_scores / (bm25_scores.max() + 1e-6)
+        
+        # 매 dense
+        q_emb = embed(query)
+        dense_scores = cosine_similarity([q_emb], self.embeddings)[0]
+        
+        # 매 fuse
+        scores = alpha * dense_scores + (1 - alpha) * bm25_norm
+        top_k = scores.argsort()[-k:][::-1]
+        return [self.examples[i] for i in top_k]
+```
+
+### Cross-encoder rerank
+```python
+from sentence_transformers import CrossEncoder
+
+reranker = CrossEncoder('cross-encoder/ms-marco-MiniLM-L-6-v2')
+
+def rerank(query, candidates, k=5):
+    pairs = [[query, c['text']] for c in candidates]
+    scores = reranker.predict(pairs)
+    sorted_idx = scores.argsort()[-k:][::-1]
+    return [candidates[i] for i in sorted_idx]
+```
+
+### Skill-aware few-shot
+```python
+def skill_aware_few_shot(query):
+    skill = classify_skill(query)  # 매 LLM classifier
+    
+    # 매 매 skill 의 specific pool
+    skill_examples = examples_by_skill[skill]
+    
+    relevant = vector_search(query, skill_examples, k=3)
+    return relevant
+```
+
+### Token budget management
+```python
+def fit_in_context(examples, max_tokens=4000, query_tokens=500):
+    """매 context window 의 fit."""
+    available = max_tokens - query_tokens
+    
+    selected = []
+    used = 0
+    for ex in examples:  # 매 already ranked
+        ex_tokens = count_tokens(ex)
+        if used + ex_tokens > available:
+            break
+        selected.append(ex)
+        used += ex_tokens
+    return selected
+```
+
+### Long-context many-shot (modern)
+```python
+def many_shot_icl(query, n_examples=100):
+    """매 100+ example 의 long context (Anthropic 2024)."""
+    # 매 simple: 매 just retrieve more
+    relevant = vectordb.similarity_search(query, k=n_examples)
+    
+    # 매 quality > quantity rerank
+    reranked = rerank(query, relevant, k=n_examples)
+    
+    return format_many_shot(reranked, query)
+```
+
+### Iterative refinement
+```python
+def iterative_few_shot(query, max_iter=3):
+    examples = initial_select(query, k=5)
+    
+    for i in range(max_iter):
+        result = llm.generate(format_prompt(examples, query))
+        critique = self_critique(result, query)
+        
+        if critique.is_satisfactory: return result
+        
+        # 매 critique 의 use 의 better example 의 retrieve
+        examples = retrieve_for_weakness(query, critique, k=5)
+    
+    return result
+```
+
+### Eval (offline)
+```python
+def eval_few_shot_strategy(strategy, eval_set):
+    correct = 0
+    for ex in eval_set:
+        examples = strategy(ex['query'])  # 매 LEAVE OUT current example
+        prompt = format_prompt(examples, ex['query'])
+        pred = llm.generate(prompt)
+        if pred == ex['answer']: correct += 1
+    return correct / len(eval_set)
+```
+
+## 매 결정 기준
+| 상황 | Strategy |
+|---|---|
+| Diverse query | Vector + MMR |
+| High accuracy | LLM-as-judge select |
+| Real-time / cost | Vector top-K only |
+| Long context | Many-shot 100+ |
+| Skill variety | Classifier + skill-specific |
+| Critical | Hybrid + cross-encoder rerank |
+
+**기본값**: Hybrid retrieve + MMR + token budget. 매 critical = 매 cross-encoder rerank.
+
+## 🔗 Graph
+- 부모: [[Prompt_Engineering|Prompt-Engineering]] · [[In-Context-Learning]] · [[RAG]]
+- 변형: [[kNN-Prompting]]
+- 응용: [[Faiss]] · [[BM25]]
+- Adjacent: [[Transformer_Architecture_and_LLM_Foundations|BERT]] · [[CLIP]] · [[Sentence-Transformers]] · [[Best-of-N_Sampling]] · [[Be-Detailed]]
+
+## 🤖 LLM 활용
+**언제**: 매 in-context learning. 매 RAG-augmented prompt. 매 task-specific accuracy boost.
+**언제 X**: 매 zero-shot capable task. 매 single template task.
+
+## ❌ 안티패턴
+- **No diversity**: 매 redundant similar example.
+- **Test data leakage**: 매 evaluation 의 inflate.
+- **Inconsistent format**: 매 confuse model.
+- **Always max examples**: 매 token waste.
+- **Static pool 의 stale**: 매 update 의 X.
+
+## 🧪 검증 / 중복
+- Verified (Liu 2022 What Makes Good In-Context Examples, Anthropic many-shot 2024).
+- 신뢰도 A.
+- Related: [[Transformer_Architecture_and_LLM_Foundations|BERT]] · [[Sentence-Transformers]] · [[Best-of-N_Sampling]] · [[Be-Detailed]] · [[ChatGPT_Emoticon_Prompt_Engineering]].
+
+## 🕓 Changelog
+| 날짜 | 변경 |
+|---|---|
+| 2026-05-08 | Phase 1 |
+| 2026-05-10 | Manual cleanup — strategy + 매 LangChain / MMR / LLM-judge / hybrid / many-shot code |