Embeddings turn content into vectors, which are lists of numbers. Similar content ends up with similar vectors, so software can search by meaning rather than exact keywords only.
In practice
In a knowledge base, documents are converted into embeddings and stored. When a user asks a question, the question is embedded too, and the system finds nearby document chunks.
What to watch
Embeddings capture similarity, not truth. Similar text can still be outdated, irrelevant, or wrong for the user question.