Mastering the Unpredictable: Testing Generative AI Systems
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Testing Generative AI
As generative AI becomes increasingly prevalent in software applications, software testers are discovering that their familiar testing methods often fall short in addressing the context-dependent risks of non-deterministic AI systems. This talk explores strategies to effectively test generative AI systems while maintaining a balance between rapid innovation, safety, and regulatory compliance.
Participants will learn approaches for testing inexplicable fuzzy AI outputs that are sometimes wrong. Attendees will gain insights into:
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Recognizing and assessing AI-related risks
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Implementing test strategies tailored to the unpredictability of AI outputs
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Establishing meaningful benchmarks to test capability and safety
This session is designed for developers, QA professionals, and product managers committed to the responsible and effective integration of generative AI into their software.
About Ben
Ben Simo is a Principal Product Researcher at Tricentis and a context-driven software quality leader with over 30 years' experience helping software development teams make informed decisions that lead to delivering better software that improves lives.
Known for his investigative and human-centered approach, Ben champions both experimental and observational testing to demonstrate capabilities and to discover risks.
At Tricentis, Ben focuses on two key areas at the intersection of AI and testing: exploring approaches to testing AI systems and developing methods that use AI to empower software testers to more efficiently and effectively test their products.
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