Vector search
Retrieving content by mathematical similarity between semantic representations.
What is Vector search?
Vector search converts queries and content into embeddings, then finds nearby items in vector space. It can retrieve conceptually related passages even when they do not share exact keywords.
In practiceWhat does this look like in a real AI answer?
A support assistant retrieves documentation about password recovery for a query phrased as cannot access my account.
Why it matters for AI visibility
AI answers can shape awareness and buying decisions before a person visits a website. Understanding vector search helps teams connect what a model says with the prompts, sources, and technical signals that influenced the result. The useful next step is to observe the evidence, compare it over time, and act on the specific gap.
How to measure or use it
- Define the scope. Choose the relevant market, prompts, models, locations, and time window.
- Keep the evidence. Save the complete answer, its citations, and the page or claim being evaluated.
- Compare patterns. Look for repeated movement across prompts and models instead of reacting to one response.
- Take a specific action. Improve the owned page, correct a claim, earn third-party inclusion, or create better evidence.