Measuring AI: How to Evaluate and Monitor Generative Models

from Agents of Intelligence · host Sam Zamany

How do we measure quality, safety, and reliability in generative AI? In this episode, we break down Evaluation and Monitoring Metrics for Generative AI, a detailed framework that helps developers ensure their AI models produce safe, accurate, and aligned content. From risk and safety assessments to custom evaluators, synthetic data, and A/B testing, we explore the best practices for monitoring AI systems using the Azure AI Foundry. If you're building or deploying AI, this episode is a must-listen to understand how to evaluate AI effectively.

What this episode covers

Evaluating generative AI isn’t just about performance—it’s about safety, reliability, and alignment with goals. This framework provides tools like AI-assisted and traditional NLP metrics, groundedness testing for retrieval-augmented generation (RAG), and synthetic data generation for adversarial testing. With A/B experimentation and real-world monitoring, developers can continuously refine AI models. Tune in to learn how to optimize AI systems while mitigating risks.

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This episode is 20 minutes long.

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This episode was published on February 7, 2025.

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