BERT-style models use only the transformer encoder and attend in both directions, so each token sees full left and right context. Pretrained by masking words and predicting them, they excel at understanding tasks: classification, sentiment, search, named-entity recognition, embeddings. They don't generate fluent long text — that's the decoder's job. Use encoders to comprehend, not to write.