RAG Decision Framework & Trade-offs Analysis
Decision Tree for RAG Architecture
1. Should You Use RAG? (vs Alternatives)
START: Do you need external/dynamic knowledge?
├─ NO → Use base LLM or fine-tuned model
└─ YES → Continue to RAG evaluation
├─ Is knowledge highly structured?
│ └─ YES → Consider Knowledge Graph + LLM
└─ NO → RAG is optimal choice
├─ Real-time updates needed?
│ ├─ YES → Dynamic RAG with API integration
│ └─ NO → Static RAG with periodic updates
└─ Multi-modal content?
├─ YES → Multi-modal RAG
└─ NO → Text-only RAG
2. RAG vs Alternative Approaches
Approach | Best For | Limitations | Cost Profile |
---|---|---|---|
Pure LLM | Creative tasks, general reasoning | Knowledge cutoff, hallucinations | Low operational cost |
Fine-tuning | Domain expertise, consistent style | Static knowledge, expensive updates | High training cost |
RAG | Dynamic knowledge, factual accuracy | Retrieval complexity, latency | Medium operational cost |
Knowledge Graph + LLM | Structured reasoning, relationships | Setup complexity, limited scope | High setup cost |
Architecture Trade-offs Matrix
Retrieval Strategy Comparison
Strategy | Precision | Recall | Latency | Complexity | Best Use Case |
---|---|---|---|---|---|
Semantic Only | Medium | High | Low | Low | General Q&A |
Keyword Only | High | Low | Very Low | Very Low | Exact matching |
Hybrid (Semantic + Keyword) | High | High | Medium | Medium | Most applications |
Multi-stage Retrieval | Very High | Medium | High | High | Critical accuracy needs |
Vector Database Trade-offs
Performance vs Cost Analysis
High Performance + High Cost:
- Pinecone: $70-200/month for 10M vectors
- Best for: Production, high-scale applications
Medium Performance + Medium Cost:
- Weaviate Cloud: $25-100/month
- Best for: Growing applications, good feature set
Low Cost + Adequate Performance:
- Chroma: $0 (self-hosted)
- Best for: Development, small-scale production
Scalability Decision Matrix
Vector Count | Recommended Solutions | Monthly Cost Estimate |
---|---|---|
< 1M vectors | Chroma, FAISS | $0-50 |
1M-10M vectors | Qdrant Cloud, Weaviate | $50-300 |
10M-100M vectors | Pinecone, Weaviate Enterprise | $300-2000 |
> 100M vectors | Custom distributed solution | $2000+ |
Chunking Strategy Decision Guide
Context-Aware Chunking Selection
def select_chunking_strategy(document_type: str, use_case: str) -> str:
"""문서 타입과 사용 사례에 따른 청킹 전략 선택"""
decision_matrix = {
('academic_paper', 'research'): 'semantic_chunking', # 섹션 단위
('technical_doc', 'qa'): 'fixed_size_overlap', # 일관된 크기
('conversation', 'chat'): 'turn_based_chunking', # 대화 턴 단위
('code', 'documentation'): 'function_based', # 함수/클래스 단위
('legal', 'compliance'): 'paragraph_chunking', # 단락 단위
('news', 'summarization'): 'sentence_clustering' # 의미 클러스터
}
return decision_matrix.get((document_type, use_case), 'fixed_size_overlap')
Chunk Size Impact Analysis
Chunk Size | Retrieval Precision | Context Completeness | Processing Cost | Best For |
---|---|---|---|---|
Small (100-300 tokens) | ★★★★★ | ★★☆☆☆ | ★★★★★ | Factual Q&A |
Medium (300-600 tokens) | ★★★★☆ | ★★★★☆ | ★★★☆☆ | General purpose |
Large (600-1000 tokens) | ★★☆☆☆ | ★★★★★ | ★★☆☆☆ | Complex reasoning |
Quality vs Performance Trade-offs
Latency Budget Allocation
Total Target Latency: 2000ms
├─ Query Processing: 50ms (2.5%)
├─ Embedding Generation: 100ms (5%)
├─ Vector Search: 200ms (10%)
├─ Document Retrieval: 150ms (7.5%)
├─ Context Preparation: 100ms (5%)
├─ LLM Generation: 1300ms (65%)
└─ Post-processing: 100ms (5%)
Quality Optimization Strategies
High-Accuracy Configuration (높은 정확도 우선)
config_high_accuracy = {
'retrieval': {
'method': 'hybrid_search',
'initial_k': 50,
'reranking': True,
'final_k': 10
},
'generation': {
'model': 'gpt-4-turbo',
'temperature': 0.1,
'max_tokens': 1000
},
'expected_latency': '5-8 seconds',
'cost_per_query': '$0.05-0.10'
}
Fast-Response Configuration (빠른 응답 우선)
config_fast_response = {
'retrieval': {
'method': 'semantic_only',
'k': 5,
'reranking': False
},
'generation': {
'model': 'gpt-3.5-turbo',
'temperature': 0.3,
'max_tokens': 500
},
'expected_latency': '1-2 seconds',
'cost_per_query': '$0.01-0.03'
}
Cost Optimization Framework
Cost Component Analysis
Monthly RAG Operation Costs (10K queries):
Embedding Generation:
- OpenAI: $13 (10K queries × $0.0013/query)
- Local model: $0 (hardware amortized)
Vector Database:
- Pinecone: $70 (starter plan)
- Chroma: $0 (self-hosted)
LLM Generation:
- GPT-4: $300-600 (depends on context length)
- GPT-3.5: $60-120
- Local LLM: $0 (hardware amortized)
Infrastructure:
- Cloud hosting: $50-200
- Self-hosted: $0-50
Total Range: $130-870/month
Cost Reduction Strategies
1. Query Optimization (20-40% cost reduction)
class CostOptimizedRAG:
def __init__(self):
self.query_cache = TTLCache(maxsize=10000, ttl=3600) # 1시간 캐시
self.embedding_cache = LRUCache(maxsize=50000)
def smart_retrieval(self, query: str):
# 1. 캐시된 쿼리 확인
if query in self.query_cache:
return self.query_cache[query]
# 2. 유사 쿼리 검색 (90% 이상 유사도)
similar_cached = self.find_similar_cached_query(query, threshold=0.9)
if similar_cached:
return self.query_cache[similar_cached]
# 3. 새로운 검색 수행
results = self.perform_retrieval(query)
self.query_cache[query] = results
return results
2. Model Selection Strategy
def select_model_by_complexity(query: str, context: str) -> str:
"""쿼리 복잡도에 따른 모델 선택으로 비용 최적화"""
complexity_score = calculate_complexity(query, context)
if complexity_score < 0.3:
return 'gpt-3.5-turbo' # 간단한 쿼리
elif complexity_score < 0.7:
return 'gpt-4' # 중간 복잡도
else:
return 'gpt-4-turbo' # 고도의 추론 필요
Scalability Planning
Growth Stage Recommendations
Stage 1: MVP (< 1K queries/day)
Architecture: Monolithic RAG
Vector DB: Chroma (local)
LLM: OpenAI API
Embedding: sentence-transformers (local)
Infrastructure: Single server
Monthly Cost: $50-100
Stage 2: Growth (1K-10K queries/day)
Architecture: Microservices
Vector DB: Qdrant Cloud
LLM: OpenAI API + caching
Embedding: OpenAI API
Infrastructure: Load balancer + 2-3 servers
Monthly Cost: $300-800
Stage 3: Scale (10K+ queries/day)
Architecture: Distributed RAG
Vector DB: Pinecone/Custom distributed
LLM: Multiple providers + routing
Embedding: Custom fine-tuned models
Infrastructure: Kubernetes cluster
Monthly Cost: $2000+
Performance Scaling Patterns
Horizontal Scaling Strategy
class DistributedRAG:
def __init__(self):
self.retrieval_cluster = RetrievalCluster([
'retrieval-node-1:8080',
'retrieval-node-2:8080',
'retrieval-node-3:8080'
])
self.generation_pool = GenerationPool([
'gpt-4-endpoint-1',
'gpt-4-endpoint-2',
'claude-endpoint-1'
])
async def distributed_query(self, query: str):
# 부하 분산된 검색
retrieval_results = await self.retrieval_cluster.search(
query,
strategy='round_robin'
)
# 모델 풀에서 최적 모델 선택
best_model = self.generation_pool.select_optimal(
context_length=len(retrieval_results),
query_complexity=self.assess_complexity(query)
)
response = await best_model.generate(query, retrieval_results)
return response
Monitoring & Optimization Decision Points
Key Metrics Thresholds
Metric | Good | Warning | Critical | Action Required |
---|---|---|---|---|
Retrieval Latency | < 200ms | 200-500ms | > 500ms | Optimize indexing |
Generation Latency | < 2s | 2-5s | > 5s | Model optimization |
Retrieval Precision@5 | > 0.8 | 0.6-0.8 | < 0.6 | Improve embeddings |
Answer Relevance | > 0.85 | 0.7-0.85 | < 0.7 | Tune prompts |
Cost per Query | < $0.05 | $0.05-0.15 | > $0.15 | Cost optimization |
Optimization Decision Tree
Performance Issue Detected
├─ High Latency?
│ ├─ Retrieval slow? → Optimize vector search
│ └─ Generation slow? → Reduce context/change model
├─ Poor Accuracy?
│ ├─ Bad retrieval? → Improve embeddings/chunking
│ └─ Bad generation? → Better prompts/model
└─ High Costs?
├─ Too many API calls? → Add caching
└─ Expensive model? → Smart model routing
Implementation Alternatives Analysis
Alternative 1: Simple Keyword-based RAG
장점 (Pros):
- 구현 간단 (Easy implementation)
- 낮은 지연시간 (Low latency)
- 정확한 키워드 매칭 (Exact keyword matching)
단점 (Cons):
- 의미적 검색 부족 (No semantic understanding)
- 동의어 처리 불가 (Cannot handle synonyms)
- 복잡한 질의 처리 한계 (Limited complex query handling)
Alternative 2: Knowledge Graph + LLM
장점 (Pros):
- 구조화된 추론 (Structured reasoning)
- 관계 기반 검색 (Relationship-based search)
- 높은 정확도 (High precision)
단점 (Cons):
- 복잡한 초기 설정 (Complex setup)
- 지식 그래프 구축 비용 (KG construction cost)
- 확장성 제한 (Scalability limitations)
Alternative 3: Fine-tuned Domain Model
장점 (Pros):
- 도메인 특화 성능 (Domain-specific performance)
- 외부 검색 불필요 (No external retrieval needed)
- 일관된 응답 스타일 (Consistent response style)
단점 (Cons):
- 정적 지식 (Static knowledge)
- 높은 훈련 비용 (High training cost)
- 업데이트 어려움 (Difficult to update)
기술 용어 사전 (Technical Terms for Children)
-
Trade-off (트레이드오프): 좋은 것을 얻기 위해 다른 것을 포기하는 것이에요. 예를 들어, 빠른 답변을 원하면 정확도를 조금 포기해야 해요.
-
Latency (지연시간): 질문을 하고 답변을 받기까지 걸리는 시간이에요. 마치 친구에게 말을 걸고 대답을 듣기까지의 시간과 같아요.
-
Scalability (확장성): 더 많은 사람들이 사용해도 잘 작동하는 능력이에요. 작은 놀이터에서 큰 놀이터로 바뀌어도 모든 아이들이 놀 수 있는 것과 같아요.
-
Precision (정밀도): 찾은 정보 중에서 정말 필요한 정보의 비율이에요. 사탕 10개 중에서 내가 좋아하는 사탕이 8개면 정밀도가 높은 거예요.
-
Caching (캐싱): 자주 사용하는 것들을 가까운 곳에 미리 저장해두는 것이에요. 자주 읽는 책을 책상 위에 두는 것과 같아요.
-
Load Balancing (부하 분산): 일이 너무 많아지면 여러 명이 나누어서 하는 것이에요. 청소를 혼자 하지 않고 친구들과 함께 나누어서 하는 것 같아요.
-
Vector Database (벡터 데이터베이스): 의미를 숫자로 바꿔서 저장하는 특별한 저장고예요. 비슷한 뜻의 글들을 빠르게 찾을 수 있어요.