Abstract: Historically, email sequences for sales and marketing must be generated manually, which is a time-intensive and labor-intensive process. Accordingly, disclosed embodiments use machine learning to automatedly generate personalized, relevant, and/or performant email sequences, at scale. In particular, an email sequence may be generated by, for each email message in an email sequence and for each content block in the email message, generate a prompt based on one or more parameter values, input the prompt to a generative language model to produce the content block, and add the content block to the email message. In an embodiment, a user may easily regenerate the entire email sequence or regenerate individual content blocks within the email sequence.