Glossary
Semantic Search
Information retrieval based on understanding query meaning and conceptual relevance rather than just matching keywords.
What is Semantic Search?
Semantic search enables AI agents to find information by understanding intent and context rather than requiring exact keyword matches. Using embeddings and neural retrieval models, semantic search captures conceptual similarity, returning relevant results even when query and document terminology differ. This dramatically improves agent access to knowledge bases and external information.
Semantic search is foundational for RAG implementations, question answering systems, and knowledge-intensive agent tasks. It handles synonyms, related concepts, and natural language variations that would confound traditional keyword search, enabling more natural agent interactions and more accurate information retrieval.
Example
A user asks "How do I cancel my subscription?" Traditional keyword search might miss documents titled "Membership Termination Guide" but semantic search understands the conceptual similarity and returns the relevant documentation despite different terminology.
How Signet addresses this
Signet's Quality dimension favors agents employing semantic search for knowledge retrieval because it improves accuracy and reduces misunderstanding. Agents using semantic techniques to ground responses in relevant information demonstrate higher quality than those relying on keyword matching.
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