Factuality controls and verification

Hallucination guardrails ensure AI-generated content doesn't contain information that is factually wrong or misleading. These guardrails help systems determine when to make definitive statements versus when to show uncertainty or cite sources. When implementing these guardrails, designers define confidence levels that shape how the AI responds based on how certain it is about information. For high-stakes topics like health or finance, stricter controls enforce source citations and clear uncertainty markers. Effective factuality design creates different types of responses, ranging from verified facts with citations to clear acknowledgments when the system is speculating.

Verification can include fact-checking against knowledge bases, requiring sources for claims, confidence scoring, and flagging statements that need verification. Well-designed systems make their confidence visible to users through both words and visual cues, helping people know how much to trust the information.