How do text embeddings work, and how are they used with vector databases in LLM applications? What factors affect embedding quality and retrieval performance?

Sample answer preview

Text embeddings convert text into dense numerical vectors that capture semantic meaning. Similar texts produce similar vectors, enabling semantic search and retrieval that goes beyond keyword matching. Embeddings are fundamental to RAG systems and many other LLM applications.