RAG Pipelines in Production: Vector Database Benchmarks, Chunking Strategies, and Hybrid Search Data

72% of enterprises now run RAG pipelines in production. That number was 8% in Q1 2024. The transition from experiment to infrastructure happened faster than any previous ML deployment pattern. Retrieval-augmented generation solves the fundamental limitation of large language models: they hallucinate when asked about data they were not trained on. RAG feeds relevant documents into the LLM's context window at query time, grounding responses in actual data. The architecture is straightforward. The production details are not. This article covers the vector database benchmarks, the chunking strategies that actually improve retrieval quality, and the hybrid search data that explains why 72% of production systems combine dense and sparse retrieval. Vector Database Latency at Scale Four databases dominate production RAG: Pinecone, Qdrant, Weaviate, and ChromaDB. They differ in architecture, deployment model, and performance characteristics. Qdrant delivers the lowest p50 latency at 6ms for 1M