
Generative AI (genAI) promises scalability, efficiency, and flexibility, but enterprises face significant hurdles in ensuring its reliability.Issues like hallucinations, imperfect training data, and models that disregard specific queries raise concerns over the accuracy of genAI outputs. Despite these challenges, organizations are actively seeking strategies to mitigate these problems and ensure the dependable performance of their AI-driven systems. Mayo Clinic’s Approach










