Vector embeddings are numerical representations that let software compare text by semantic similarity rather than exact keywords.
Embeddings turn language into vectors so systems can find related passages even when the words are different. That is why they are foundational for semantic search and many RAG pipelines.
They help retrieval systems find meaning, not just string matches.
Embeddings alone do not provide scope boundaries, durable user memory, or product-level state management. They are one ingredient in a retrieval system, not the full answer to continuity.
This is why teams that start with embeddings often end up adding a memory layer later.
Use these glossary pages and commercial landing pages to move from definition to implementation.