Metrics to Detect Hallucinations with Pradeep Javangula

This AI Explained podcast features Josh Rubin from Fiddler AI interviewing Pradeep Javangula, Chief AI Officer at RagaAI, about metrics for detecting hallucinations in large language models (LLMs). Pradeep defines "hallucination" as the generation of untruthful or contextually irrelevant content by AI models, emphasizing that these models predict the next token without understanding truth. The discussion covers the challenges enterprises face in deploying LLMs for specific problems, the importance of pre-deployment testing, and the need for comprehensive observability to identify and remediate issues. They explore various evaluation methods, including human feedback, statistical models, and the use of LLMs for evaluation, while also addressing the importance of data quality, context relevance, and the detection of malicious intent. The conversation highlights the rapid evolution of AI and the need for careful infrastructure choices and a healthy skepticism towards generated content.