Implementing RAG Systems: From Prototype to Production
A comprehensive guide to building Retrieval-Augmented Generation systems. Covers chunking strategies, embedding models, vector databases, reranking, evaluation, and production hardening.
Implementing RAG Systems: From Prototype to Production
RAG combines the knowledge of LLMs with your private data. Here's how to build it right.
Architecture Overview
User Query → Embedding → Vector Search → Rerank → Context → LLM → Answer
Chunking Strategies
Fixed-Size with Overlap
Semantic Chunking
Split on semantic boundaries (headings, paragraphs).
Embedding Models
| Model | Dimensions | Use Case | |-------|------------|----------| | text-embedding-3-small | 1536 | General purpose | | text-embedding-3-large | 3072 | High accuracy | | bge-large-en-v1.5 | 1024 | Open source, strong |
Vector Database Comparison
- pgvector — PostgreSQL extension, simple ops
- Pinecone — Managed, serverless
- Weaviate — Graph + vector, flexible
- Qdrant — Rust-based, fast, filtering
Reranking
Evaluation Framework
Production Hardening
- Circuit breakers for LLM calls
- Request/response logging for debugging
- Cost tracking per query
- A/B testing framework
- Gradual rollout with feature flags
Conclusion
RAG is more than vector search. Production systems need evaluation, monitoring, and iterative improvement. Start with a solid foundation, then optimize each component.
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