We often see strong validation accuracy during training, yet performance drops once the model faces real-world inputs.

For example:

from sklearn.model_selection import train_test_split
from sklearn.metrics import accuracy_score
from transformers import AutoTokenizer, AutoModelForSequenceClassification, Trainer, TrainingArguments
import numpy as np

# Split dataset
train_texts, val_texts, train_labels, val_labels = train_test_split(
    texts, labels, test_size=0.2, random_state=42
)

# Standard training setup
training_args = TrainingArguments(
    output_dir="./results",
    evaluation_strategy="epoch",
    per_device_train_batch_size=16,
    per_device_eval_batch_size=16,
    num_train_epochs=3
)

# After training
predictions = trainer.predict(val_dataset)
val_preds = np.argmax(predictions.predictions, axis=1)

print("Validation Accuracy:", accuracy_score(val_labels, val_preds))

Validation accuracy may look strong here. But once deployed, inputs can differ in tone, structure, vocabulary, or intent.

So the real question is:

Are we validating for real-world variability, or just for dataset consistency?

What practical steps do you take to simulate production conditions during evaluation?

Would appreciate insights from teams deploying NLP systems at scale.