The vector database you choose for your RAG system determines more than retrieval speed — it shapes your architecture's scalability ceiling, operational complexity, cost model, and what filtering capabilities you have at query time. This is the production comparison for teams choosing in 2026.
Chunking directly affects retrieval quality, answer grounding, latency, and cost in RAG systems. This guide explains five practical chunking strategies, when to use each one, and how to evaluate chunk quality in production pipelines.
A production RAG system is more than a vector database connected to an LLM. This guide breaks down the core components: ingestion, chunking, embeddings, retrieval, reranking, prompt assembly, generation, evaluation, observability, and governance.
Bhanu Pratap Singh