The main issue is usually distribution shift between validation and production data. You can simulate this and detect it with simple evaluation checks.

For example, compare validation vs production predictions:

import numpy as np
from sklearn.metrics import accuracy_score

# validation performance
val_preds = model.predict(X_val)
val_acc = accuracy_score(y_val, val_preds)

# production-like sample
prod_preds = model.predict(X_prod_sample)
prod_acc = accuracy_score(y_prod_sample, prod_preds)

print("Validation Accuracy:", val_acc)
print("Production Accuracy:", prod_acc)

If there’s a large gap, you likely have:

• Overfitting

• Data drift

• Label mismatch

• Input distribution differences

You can also monitor drift directly:

import scipy.stats as stats

# Compare token length distributions
stats.ks_2samp(val_token_lengths, prod_token_lengths)

A significant difference suggests distribution shift.

Strong validation metrics do not guarantee robustness. Always evaluate on out-of-distribution samples and monitor drift continuously after deployment.