Generative AI is a powerful asset if you know how to tame it. As this technology rapidly transforms the software landscape, one of the key challenges lies in effectively testing and validating GenAI applications. Traditional testing methodologies fall short in addressing the unique complexities posed by these systems, especially in enterprise environments.

Drawing from real-world experiences and hard-earned insights, we'll explore how to adapt established software engineering principles to the world of GenAI, and where entirely new approaches are necessary. Key topics include:

• Strategies for testing non-deterministic outputs
• Which metrics to choose and how to cope with conflicting metrics
• Unit-Tests vs End-To-End-Tests in the World of GenAI
• How to evaluate the evaluators: Aligning automatic evaluation with human evaluation
• Techniques for evaluating model hallucinations and biases
• Approaches to performance and scalability testing for GenAI systems
• Methods for ensuring data privacy and security in AI-driven applications
• Practical tools and frameworks for continuous testing in GenAI pipelines

Attendees will gain actionable knowledge on implementing robust testing practices for GenAI applications, bridging the gap between traditional QA and the demands of modern AI systems. This session is essential for developers, QA engineers, and technical leaders navigating the complexities of deploying GenAI in production environments.

Target Audience: AI engineers, software engineers, architects, and stakeholders working with GenAI applications
Prerequisites: Attendees should have a foundational understanding of AI
Level: Advanced