How AI answers are made
Embeddings
Also called: Vector embeddings
An embedding is a long list of numbers that represents the meaning of a piece of text, so a computer can measure how related two texts are.
In plain words
OpenAI describes an embedding as a vector: a list of numbers. The distance between two of those lists shows how related the two texts are.
Texts with a similar meaning end up close together, even when they use different words.
Example
The questions "cheap running shoes" and "budget sneakers for jogging" share almost no words. Their embeddings would still sit close together, because they mean nearly the same thing.
Why it matters for AI search
Many retrieval systems turn stored passages and the incoming question into embeddings. Then they return the passages that sit closest to the question.
That is how a page can be found for a question it never literally contains.
What you can do
- Explain your topic in plain language, not only in jargon.
- Keep each section about one thing, so its meaning is clear.
- Use the common names for things next to your own product names.
Often confused with
- Keywords
- Keyword matching looks for the same words. Embeddings compare meaning. Many systems use both.
Sources
Facts and sources last checked on 19 October 2026.
Related terms
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