In this session, we’ll explore how systematic evaluation ensures generative AI applications are reliable, safe, and effective across their lifecycle. From selecting the right base model to rigorous pre-production testing and ongoing post-deployment monitoring, evaluation helps teams address risks like misinformation, biases, and security vulnerabilities. Learn how to integrate evaluation into every stage of development to build AI solutions that deliver high-quality user experiences, foster trust, and adapt seamlessly to real-world demands.

In the dynamic world of artificial intelligence, evaluation is the cornerstone of building reliable, safe, and impactful generative AI applications. From selecting the right base model to pre-production testing and post-deployment monitoring, evaluation ensures that AI systems not only meet technical and ethical standards but also adapt to real-world demands. A robust evaluation framework enables organizations to address risks such as misinformation, harmful biases, or security vulnerabilities while optimizing for quality, relevance, and safety. By leveraging tools like Azure AI Foundry and Evaluation SDKs, teams can systematically test model outputs, simulate edge cases, and measure performance across critical metrics. This iterative process is pivotal for ensuring that AI applications deliver consistent, high-quality user experiences while fostering trust in AI-driven solutions. As the foundation of GenAIOps, evaluation transforms the AI lifecycle into a reliable and repeatable process, enabling organizations to innovate responsibly and effectively.