I recently saw a discussion where a model achieved surprisingly high validation performance, only to fail badly in production.

The root cause wasn’t the algorithm, it was feature engineering.

Some of the engineered features were technically available during training but relied on information that wouldn’t exist at prediction time.

The line between clever feature engineering and subtle leakage isn’t always obvious.

How do you evaluate whether a feature is introducing future information, target leakage, or unrealistic signals?

Have you encountered cases where leakage survived code reviews and validation checks?