A general-purpose AI model knows a lot about many things. A domain-specific AI knows a lot about one thing well — insurance underwriting, manufacturing quality control, banking credit risk. Domain specificity is achieved through fine-tuning, retrieval from domain knowledge bases, specialized prompting, or purpose-built models.
Generic models struggle with specialized terminology, specific regulations, and proprietary processes. Domain-specific AI gets more accurate answers, produces fewer hallucinations, and handles edge cases more reliably within its domain. The investment in domain specificity typically pays off quickly in reduced error rates and less human review overhead.
