#hallucination detection

Learn how to build a comprehensive AI agent evaluation framework that assesses performance, safety, and reliability using advanced metrics, batch processing, and visual dashboards.

ETH and Stanford researchers developed MIRIAD, a 5.8 million pair medical QA dataset grounded in peer-reviewed literature, improving LLM accuracy and hallucination detection in medical AI.

AWS’s Automated Evaluation Framework offers a scalable, automated solution to improve the accuracy, fairness, and safety of Large Language Models, setting a new benchmark in AI reliability.