Unlocking Semantic Understanding: Vector Embeddings and Similarity Search on AWS

Vector embeddings transform unstructured data into numerical representations that preserve semantic meaning, enabling search systems to understand intent rather than just matching keywords. This foundational knowledge is essential for building production-grade vector databases and modern AI applications like RAG systems and intelligent chatbots.