When text is converted to embeddings, semantically similar phrases produce vectors that are close together in high-dimensional space. This allows a RAG system to retrieve documents about 'AI automation' even if the query uses different words like 'agentic workflows.' Embeddings are generated by models like OpenAI's text-embedding-3 and stored in vector databases for efficient retrieval.