Semantic Memory
AI knowledge base containing factual information, policies, procedures, and conceptual understanding. Distinct from episodic memory of specific interactions. Enables AI to answer questions based on learned information rather than just pattern matching.
Knowledge Representation in AI
Semantic memory represents an AI system's knowledge of facts, concepts, and relationships. This includes product information, company policies, troubleshooting procedures, and general understanding of how things work. It's the 'what we know' layer that enables AI to answer questions and provide accurate information.
In customer service AI, semantic memory comes from multiple sources: knowledge base articles, product documentation, policy documents, and training data. The AI organizes this information conceptually, understanding relationships between topics and inferring answers even when questions don't match documentation exactly.
How Semantic Memory Works
Modern semantic memory systems use vector embeddings to represent knowledge. This allows AI to understand conceptual similarity: recognizing that questions about 'refunds,' 'money back,' and 'getting a credit' all relate to the same underlying policy, even though the words differ. The system retrieves relevant information based on meaning, not just keyword matching.
- Semantic search: Find information based on meaning and intent
- Knowledge synthesis: Combine information from multiple sources
- Conceptual reasoning: Draw inferences beyond explicit documentation
Keeping Semantic Memory Current
Semantic memory requires ongoing maintenance. As products evolve, policies change, and new information emerges, the knowledge base must stay current. Leading AI platforms automate this: syncing with knowledge management systems, identifying outdated information, and flagging gaps. The quality of semantic memory directly determines AI accuracy—comprehensive, well-organized knowledge enables confident automated resolution.