> LLMs are always going to generate output (prose or code) in the same style as the training data. There's also an implicit bias towards blathering in LLM outp

> LLMs are always going to generate output (prose or code) in the same style as the training data. There's also an implicit bias towards blathering in LLM output, especially in open-ended scenarios like code completion. Language models prefer high-probability outputs; if it's appropriate for a model to generate highly stereotypical content like boilerplate getters/setters and documentation comments, it's more likely to generate that than something more application-specific like the actual implementation of an algorithm.