Exploring how curatorial practices from museum exhibition production can provide a fresh perspective on testing GenAI solutions, embracing probabilistic outcomes and interpretive approaches.

IOTech Hub's resource offers insights, tutorials, and resources for Internet of Things (IoT) enthusiasts and developers. Readers can learn about IoT technologies, hardware platforms, and application development. With articles, tutorials, and case studies, IOTech Hub provides  guidance and expertise for building connected devices and IoT solutions.

iO tech_hub

Software testing for GenAI requires a paradigm shift from deterministic verification to interpretive, probabilistic approaches. Drawing parallels between museum exhibition curation and GenAI testing, the author proposes 'curatorial testing' as a methodology that embraces multiple valid interpretations, semantic evaluation, and continuous assessment. This approach acknowledges that GenAI outputs are probabilistic and context-dependent, requiring diverse testing teams to explore the probability distribution of outcomes rather than binary pass/fail criteria. Key considerations include understanding training data, prompts, model updates, and other parameters that influence generative outputs, while adapting traditional testing practices to handle the stochastic nature of AI systems.

The pivot: Testing through new lenses?