Glossary
Vector Database
A vector database stores embeddings and retrieves items whose vectors are similar to a query vector. It is commonly used in retrieval-augmented generation systems to find relevant passages, although public AI search products may combine vector stores with conventional search indexes and other systems.
In plain terms
It stores meaning-based representations so related passages can be found quickly.
Why it matters
The concept shows why coherent, focused chunks are useful for AI retrieval, without implying that every engine uses the same database.
How to apply it
- Structure pages into focused sections.
- Attach source URLs and metadata to indexed chunks.
- Evaluate retrieval with realistic questions.
Example
A support assistant embeds documentation sections in a vector store and retrieves the closest passages before answering.
Sources
Related reading
Related terms
Back to the full glossary (75 terms).
Ranking is no longer enough
You need to be cited, mentioned, and recommended.
Being cited, mentioned, and recommended are three different outcomes, and most brands only ever achieve the first one. Ranking is no longer enough because AI engines answer buyers directly and name only a short list of vendors as the recommendation — everyone else is cited in passing, if at all. Get a free AI Visibility Report to see exactly where your brand appears today across ChatGPT, Google AI Overviews, Gemini, Perplexity, and Copilot, where competitors are winning the recommendation instead, and what's keeping you from moving up the shortlist.