Semantic Search
Search that understands the meaning and intent behind queries rather than just matching keywords.
In Depth
Semantic search is an information retrieval technique that goes beyond keyword matching to understand the meaning, intent, and context of a query. Instead of looking for exact word matches, semantic search uses embeddings and natural language understanding to find results that are conceptually relevant. For example, searching for "staff turnover" would also find documents about "employee attrition" or "workforce churn." Semantic search is powered by embedding models that convert both queries and documents into vector representations, then finds the closest matches in vector space. It is widely used in enterprise search, e-commerce, knowledge bases, and AI-powered database interfaces.
How AI for Database Helps
AI for Database uses semantic search to understand your intent even when your terminology differs from your database column names.
Related Terms
Embedding
A numerical vector representation of text, data, or objects that captures semantic meaning for AI processing.
Vector Database
A database designed to store and efficiently query high-dimensional vector embeddings for similarity search.
Natural Language Processing
A branch of AI focused on enabling computers to understand, interpret, and generate human language.
RAG
Retrieval-Augmented Generation—an AI technique that enhances LLM responses by retrieving relevant context from external data sources.
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