Explain how embeddings work in modern LLMs — from token embeddings to contextual representations. Cover: how embedding dimensions are chosen, what information is encoded in embedding space, how embeddings evolve through transformer layers, the difference between static and contextual embeddings, and why cosine similarity works for semantic search. Then explain practical implications for building RAG systems — what makes a good embedding model, and when to use general vs domain-specific embeddings.