AI & Web Glossary
Embeddings
Embeddings are lists of numbers that represent the meaning of a piece of text, so that similar ideas end up numerically close together. They are what lets AI search by meaning instead of by exact keywords.
An embedding is what you get when an AI model converts text into a long list of numbers that captures its meaning. The useful property: texts that mean similar things get similar numbers. 'My invoice is wrong' and 'there's a billing error' look nothing alike as words, but their embeddings sit close together.
This is the machinery behind meaning-based search. Embed all your documents once, embed each incoming question, and finding relevant material becomes a fast numeric comparison instead of a keyword match. Every 'ask questions of your documents' system rests on this, with the embeddings stored in a vector database and the answers composed through retrieval-augmented generation.
Practical example: a customer searches a help center for 'card got charged twice.' Keyword search misses the article titled 'Resolving duplicate payments.' Embedding search finds it immediately, because the meanings match even though the words don't. That gap is why keyword-only search frustrates people and meaning-based search doesn't.
Where this shows up in practice
- Our Knowledge & Document AI service puts this to work for small and mid-sized businesses.
- From the blog: The unglamorous work of document AI