What it means
Specialized storage optimized for similarity search over millions of vectors, often with metadata filters (user, folder, date).
Why designers should care
Design for index lag (“Docs updated 5 min ago may not appear”), access control (users only retrieve what they can read), and multi-source fusion in the UI.
Example
A copilot searches Slack, Notion, and Google Drive separately, then shows three labeled source groups so users know which system supplied each citation.
Common mistakes
- Assuming all connected sources are searchable immediately after connect.
- Retrieval across permissions users should not see. Fixed in backend, but surfaced as confusing citations.
- One blended result list with no source system or date metadata.
Frequently asked questions
What is Vector Database?
A vector database stores embeddings and retrieves the nearest matches quickly: the infrastructure behind semantic search and RAG at scale.
Why should designers care about Vector Database?
Design for index lag (“Docs updated 5 min ago may not appear”), access control (users only retrieve what they can read), and multi-source fusion in the UI.
What are common mistakes with Vector Database?
Assuming all connected sources are searchable immediately after connect; Retrieval across permissions users should not see. Fixed in backend, but surfaced as confusing citations; One blended result list with no source system or date metadata.