How does vector database work in LLM systems?

Updated May 16, 2026

Short answer

Vector databases store embeddings and enable similarity-based search for LLM applications.

Deep explanation

Vector databases store high-dimensional embeddings and retrieve nearest neighbors using similarity metrics like cosine distance. They are critical in RAG systems for retrieving relevant knowledge.

Real-world example

Searching similar customer support tickets.

Common mistakes

Using keyword search instead of semantic search.

Follow-up questions

What is embedding similarity?
Why not use SQL for this?

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