AI Glossary
What it means, why it matters to your business, and where it shows up in a real build decision.
In plain terms
A vector database stores embeddings and finds the closest matches quickly, so an AI system can retrieve the most relevant information in real time. A vector database is the storage layer that makes semantic search and RAG fast at high volume. For small datasets you may not need a dedicated one; at enterprise volume you will.
A vector database is a library where books are shelved not by title but by topic proximity. Finding everything related to contract termination is instant, even if no book uses those exact words.
A legal tech startup stores thousands of case summaries as vectors. Their AI retrieves the five most relevant precedents for any new query in milliseconds, powering a real-time research assistant.
When your RAG system operates at meaningful volume or needs sub-second retrieval. At small scale, a simple file-based approach may suffice until volume demands the upgrade.
Before you have defined and tested your embedding and retrieval strategy. A vector database is infrastructure, not a strategy. Get the retrieval logic right on small data first.
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