You've learned that Generative AI naturally can give different responses to the same question - it's simply how these systems work. But this creates a fascinating puzzle: if you're relying on Gen AI for important tasks, how do you actually check whether it's working correctly?

Think about checking whether any tool is working properly. Usually, you can verify performance by expecting consistent, predictable results. But with Generative AI, asking the same question twice might give you two different answers that are both perfectly valid.

So how do you determine if machine intelligence is performing well? How do you distinguish between helpful creative variation and actual problems when responses naturally differ each time?

This challenge becomes even more complex when businesses need to rely on these systems for important operations. How do companies ensure their AI tools are working properly when the very nature of these systems is to be somewhat variable?

This isn't just a technical challenge - it's reshaping how we think about accuracy, consistency, and what it means for a system to work "correctly" in the first place.

Join Ash Stuart as he reveals the hidden challenge of measuring Generative AI performance, and why checking whether these systems work properly might require rethinking what "correct" actually means.

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