Name: Why AI models fail in real world: Blind spots in evaluation | Daryle Serrant posted on the topic | LinkedIn
Uploaded: 2025-11-04T17:00:05.892Z
Duration: 1 min 18 s
Channel: Daryle Serrant
Description: Ever built an AI application that crushed internal testing, only to fall apart after launch? If so, your evaluation metrics might be telling you the wrong story. In my latest video, I break down the biggest blind spots in AI evaluation. The kind of mistakes that cause high-performing models to fail in the real world. Watch here: https://lnkd.in/gHWQCbB2

Daryle Serrant

AI Demystified | Transforming Businesses from Bewildered to Brilliant | byteSolid Solutions

Ever built an AI application that crushed internal testing, only to fall apart after launch? If so, your evaluation metrics might be telling you the wrong story. In my latest video, I break down the biggest blind spots in AI evaluation. The kind of mistakes that cause high-performing models to fail in the real world. Watch here: https://lnkd.in/gHWQCbB2

Transcript

And data science and machine learning. We call this the accuracy paradox. Or a seemingly high score heights a serious failure. And it doesn't stop there. The same issue shows up in how companies evaluate large language models. Teams often look at Elum benchmark scores. You know the numbers you see on leaderboards across sites like Hugging Face, and they treat them as proof of real world capability. Benchmarks like MMO U Glue and Hella Swag are great for measuring general language understanding or reasonability. But they don't tell you how well the model will perform in your business context. They sound like they do, but they really don't. In fact, the benchmarks have some serious limitations. For one thing, their scores are often inflated by data contamination. This is where parts of the test data slip into the model training data, giving artificially high results. And secondly, recent research shows that leaderboard rankings can reflect overfitting or selective reporting rather than real-world progress. So a model that dominates the leaderboard might still stumble when it's applied to real-world scenarios. That's why relying solely on benchmark scores or raw accuracy gives you a false sense of security.

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