When Search Gets Smart: Why Modern Similarity Needs Both Words and Vectors

As users increasingly initiate searches from existing documents rather than blank queries, finding similar content has become essential for modern search systems. Vector embeddings enable semantic understanding across different phrasings, but traditional lexical methods remain vital for exact-match scenarios like error codes and product SKUs. Understanding when to apply each approach determines whether a search system is merely functional or genuinely effective.