snorkel-tutorials | A collection of tutorials for Snorkel | Learning library

From your linked snorkel tutorial, the labeling functions (which maps input to labels ("HAM", "SPAM", "Abstain") are used to provide labels instead of features.

IIUC, the idea is to generate labels when you do not have good quality human labels. Though these "auto-generated" labels would be quite noisy, it could be served as a starting point of a labeled dataset. The learning process is to take this dataset and learn a model, which encodes the knowledge embedded in these labeling functions. Hopefully the model could be more general and the model could be applied to unseen data.

If some of these labeling function (can be considered as fixed rules instead) are very stable (regarding prediction accuracy) in certain conditions, given enough training data, your model should be able to learn that. However, in production system, to overcome the possibility of model instability, one easy fix is to override machine prediction with human labels on seen data. The same idea can be applied too if you think these labeling functions could be used for some specific input (pattern). In this case, the labeling functions would be used to directly get labels to override machine predictions. This process can be implemented as a pre-check before your machine-learned model runs.