Definition
Semantic Search is a search technique that understands the meaning and intent behind a query, rather than matching only on the exact keywords used, typically by converting text into vector embeddings that capture meaning.
Importance
Keyword search fails whenever a user's query doesn't use the exact words present in the content they're looking for, which happens constantly in practice, and it's a common source of retrieval failures underneath AI systems specifically, since an AI application is only as good as the information it manages to retrieve. The practical standard for enterprise search has shifted to hybrid approaches that combine semantic and keyword matching together, since pure semantic search alone can still miss exact term or code lookups that keyword matching handles better.
Application
We implement semantic search using vector embeddings and vector databases as the retrieval layer underneath RAG systems, AI assistants, and internal knowledge search, typically combined with keyword matching in a hybrid approach rather than relying on semantic search alone. This is particularly important for AI applications specifically, where retrieval quality has more effect on the reliability of the final output than most other parts of the system, so we treat search quality as a first class engineering concern rather than a default configuration left unexamined once it's technically working.
