LLM Systems
RAG System Design β Building Production-Ready Retrieval Systems
Building production-quality RAG systems requires careful consideration of retrieval strategy, evaluation, and architecture. This guide covers advanced RAG design patterns including hybrid search, re-ranking, and evaluation metrics.
- Hybrid Search β Combine keyword and semantic retrieval for better coverage
- Re-ranking β Cross-encoder re-rankers significantly improve retrieval quality
- Production Monitoring β Track faithfulness, latency, and answer relevance at scale
Great retrieval is the difference between an answer and the right answer.
RAG System Design
Building production-quality RAG systems requires careful consideration of retrieval strategy, evaluation, and architecture. This tutorial covers advanced RAG design patterns and evaluation metrics.
Advanced Retrieval Strategies
Hybrid Search
Combine sparse (keyword) and dense (semantic) retrieval:
Re-ranking
After initial retrieval, re-rank results using a cross-encoder:
Query Expansion
Expand the query to capture more relevant documents:
def expand_query(query, llm, n_expansions=3):
prompt = f"""Generate {n_expansions} alternative phrasings of this query:
Original: {query}
Alternatives:"""
response = llm.generate(prompt)
expanded = [query] + response.split("\n")
return expanded
Multi-Step Retrieval
Iteratively refine retrieval based on intermediate reasoning:
Chunking Strategies
Fixed-Size Chunking
Split documents into fixed-size chunks with optional overlap:
Semantic Chunking
Split documents at semantic boundaries (paragraphs, sections, topics):
Recursive Chunking
Split documents hierarchically, using larger separators first:
def recursive_split(text, separators=["\n\n", "\n", ". ", " "], chunk_size=512):
for sep in separators:
parts = text.split(sep)
chunks = []
current = ""
for part in parts:
if len(current) + len(part) < chunk_size:
current += sep + part if current else part
else:
if current:
chunks.append(current)
current = part
if current:
chunks.append(current)
if all(len(c) <= chunk_size for c in chunks):
return chunks
return [text]
Evaluation Metrics
Retrieval Metrics
Generation Metrics
Production RAG Architecture
Components
- Document Processing Pipeline: Ingestion, chunking, embedding, indexing
- Query Processing: Query understanding, expansion, routing
- Retrieval Layer: Hybrid search with re-ranking
- Generation Layer: LLM with context integration
- Post-processing: Answer validation, citation generation
- Monitoring: Latency, quality, drift detection
Scaling Considerations
| Component | Small Scale | Production Scale |
|---|---|---|
| Documents | < 100K | > 10M |
| Queries/sec | < 10 | > 1000 |
| Latency target | < 5s | < 500ms |
| Vector DB | ChromaDB | Pinecone/Qdrant |
| Embedding | Local model | API (OpenAI/Cohere) |
| LLM | Self-hosted | API with fallback |
Practice Exercises
- Hybrid Search: Implement hybrid search combining BM25 and FAISS. Compare with dense-only retrieval.
- Re-ranking: Add a cross-encoder re-ranker to your RAG pipeline. Measure improvement in precision@5.
- Evaluation: Build a gold standard evaluation set with 100 queries. Measure precision, recall, MRR, and faithfulness.
- Production: Design a RAG system for a knowledge base with 1M documents. Address latency, scalability, and cost.
What to Learn Next
-> Retrieval-Augmented Generation Combining LLMs with external knowledge for accurate, cited answers.
-> Prompt Engineering Getting the most out of language models through effective input design.
-> In-Context Learning Teaching LLMs new tasks without trainingβpurely through prompts.
-> LLM Agent Frameworks Building autonomous agents that reason, plan, and act.
-> LLM Inference Optimization Speeding up model inference for production deployment.
-> Building Production LLM Apps From prototype to production: deploying LLMs at scale.