Testing AI Outputs
Traditional apps rely on strong typing, making their inputs and outputs predictable. In contrast, AI applications, especially those using agents, introduce a level of unpredictability. The discussion highlights the importance of using LLMs for assessing output quality and hallucination scores, allowing for customized evaluation metrics to better understand AI behavior.In this clip
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📆 ThursdAI - Jan 2 - is 25' the year of AI agents?
Related Questions
Is the large language model (LLM) in the episode AI and the Practice of Law: from CaseText to CoCounsel, with Pablo Arredondo, VP of CoCounsel and the clip AI Reliability Metrics subject to hallucinations?
I have a question about the episode Holistic Evaluation of Generative AI Systems // Jineet Doshi // #280 and the clip Evaluating AI Reasoning. Have you seen a way to unit test large language models (LLMs) that are super helpful, as discussed in the episode How to Systematically Test and Evaluate Your LLMs Apps // Gideon Mendels // #269?