Sentence production is the uniquely human ability to transform complex thoughts into strings of words. Despite the importance of this process, language production research has primarily focused on single words. It remains an untested assumption that insights from this literature generalize to more naturalistic utterances like sentences. Here, we investigate this using high-resolution neurosurgical recordings (ECoG) and an overt production experiment where patients produce six words in isolation (picture naming) and in sentences (scene description). We used machine learning models to identify the unique brain activity pattern for each word during picture naming, and used these patterns to decode which words patients were processing while they produced sentences. In sensorimotor cortex, this procedure predicted each noun in the order it was said in the sentence, confirming that words share cortical representations across tasks. However, in inferior and middle frontal gyri (IFG and MFG), the order in which words were processed depended on the syntactic structure of the sentence. This dynamic interplay between sentence structure and word processing reveals that sentence production is not simply a sequence of single word production tasks. We argue that it is time for the field to leverage the extensive literature on word production for studying more naturalistic linguistic constructs like sentences.