Watch our recent webinar featuring Liliya Lavitas, Alexei Robsky, and Yueqing Wang, authors of Reliable Evaluations for LLMs and AI Agents, alongside Springer Nature's Director Portfolio & Product Management, Robin Padilla. This webinar gives an insightful discussion on one of the most critical, and often overlooked, aspects of enterprise AI adoption: evaluation.
This expert panel discussion explores:
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Why high-performing AI models often fail in production environments
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Why benchmark scores are poor predictors of real-world success
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Why AI evaluation should be treated as an engineering and R&D discipline, not just a testing exercise
The conversation shifts from evaluating standalone LLMs to assessing complex AI agents, while offering practical guidance on building robust evaluation capabilities. The session concludes with a discussion on trustworthiness in AI systems, the relationship between data quality and model reliability, and how organizations can establish evaluation frameworks that support safe, effective, and scalable AI deployment in real-world environments.
