Because real-world language is messy and unpredictable.

In testing, NLP models work on clean, structured, and often curated datasets.
In production, they face ambiguity, slang, domain shifts, noisy inputs, and edge cases they weren’t trained on.

There’s also a gap between benchmark performance and real user behavior.

So it’s not that models are weak.
It’s that real-world complexity is much higher than controlled test environments.