LLMs confidently generate false information—hallucinations—that can mislead users and corrupt downstream systems. In production applications, hallucinations create legal, reputational, and operational risks. Multiple techniques combined reduce hallucination frequency while enabling detection when they occur.
Prevention Strategies
RAG grounds responses in retrieved documents, reducing fabrication. Chain-of-thought prompting encourages step-by-step reasoning exposing logical errors. Lower temperature settings reduce creative variation favoring consistent outputs. Explicit instructions to acknowledge uncertainty help models express appropriate confidence levels.
- Use RAG to ground responses in verified source documents
- Implement fact-checking by asking models to verify their own claims
- Request citations and validate them against actual sources
- Set lower temperature for factual queries, higher for creative tasks
- Train users to verify critical information independently
Detection and Handling
Automated detection compares outputs against known facts. Confidence scoring identifies uncertain responses for human review. User feedback loops capture hallucinations in production. Design systems assuming hallucinations will occur, with appropriate guardrails preventing harmful consequences.