The workflow builds, trains, and saves an RNN with an LSTM layer to generate new fictive fairy tales. The brown nodes define the network structure. The "Pre-Processing" metanode reads fairy tales and index-encodes them, and creates semi-overlapping sequences. The Keras Network Learner node trains the network using the index-encoded fairy tales. Finally, the trained network is converted into a TensorFlow model, and saved to a file.