What are embeddings in AI?

An embedding turns a piece of text into a list of numbers that captures meaning, so the system can find two passages that say the same thing in different words. A keyword search misses that. Meanings match even when the nouns do not.

You will not see embeddings on a dashboard. You see the effect: a customer writes I want to stop my plan and the system brings back the cancellation policy, not an article about freight. If that match is wrong, the rest of the AI looks wrong too, because it answered from the passage it was handed.

Think of it this way: Embeddings turn meaning into coordinates on a map. Related concepts cluster nearby; unrelated ones sit far apart. That is how a search for 'cancel my account' can find a document titled 'subscription termination policy.'

A B2B SaaS company reindexes its entire documentation library as embeddings. Support queries now surface the right article even when the user's phrasing matches nothing in the title or tags.

A university help desk search used to require the official name of a form. Students typed how do I drop a class and got nothing. With embeddings, the same question surfaces the withdrawal policy, because the meaning is close even though the words differ.

Whenever the system has to find a document by meaning, not by exact words. That includes an assistant that answers from your policies, contracts, or product docs. For exact-match lookups, such as a database query by ID, or for very short structured data where traditional keyword search is simpler and faster.

RaftLabs points the model at your documents and your rules, then checks the answers against cases you already trust. That surrounding work is where these projects succeed or stall. The related work on our side is Semantic search.

This sits with the other building & tuning terms on the glossary. How a general model gets pointed at your documents, your tone, and your workflow. Worth reading next: Fine-tuning, Retrieval-Augmented Generation (RAG), and Prompt Engineering.