AI Hallucination Risk Detector

Large language models (LLMs) are increasingly embedded in high-stakes workflows — medicine, law, journalism, finance, and education — yet they remain prone to hallucination: generating fluent, confident-sounding statements that are factually false or unsupported by evidence. Current mitigation strategies (better training data, retrieval augmentation, human review) reduce but do not eliminate this risk, and end users have no reliable, real-time way to know which specific sentence in a long AI-generated response is trustworthy and which is not.

Built With

• word