Vector DB · Semantic Search · RAG
Vector databases for semantic search: patterns and pitfalls
How to choose Pinecone, FAISS or Chroma, chunk text effectively, use metadata filters, and evaluate retrieval quality for production RAG.
Puneet Singhal
· 1 min read
Practical guidance for production-grade retrieval augmented generation: embedding choices, chunking strategies, metadata filters, and retrieval evaluation. Learn how to keep semantic search fast, accurate, and cost-effective at scale.
Selecting a store
- Pinecone for managed scale and filtering; FAISS for on-premises speed; Chroma for simplicity and prototyping
- Prioritize metadata filtering, hybrid search (sparse + dense), and strong consistency guarantees
- Plan for namespace or tenant isolation, retention policies, and encryption at rest/in transit
Data prep & retrieval
- Chunk by semantic boundaries; keep overlap small but meaningful for context stitching
- Normalize and deduplicate content; enrich with metadata for natural filtering and freshness scoring
- Use rerankers or cross-encoders when precision matters; cache stable lookups near the application
Evaluation & monitoring
- Track recall@k, precision@k, latency, and cost per query in dashboards
- Build labeled test sets; run offline evaluations and shadow traffic before rollout
- Continuously improve with human feedback loops and embeddings refresh schedules
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