A general-purpose large language model (ChatGPT-GPT-4o) was used to generate self-relevant (SR) and non-self-relevant (NSR) vignettes designed to elicit the N400 component, a neurophysiological marker of semantic and affective processing. The Vignette Generation Tool (VGT) systematically controlled linguistic variables to ensure structural and semantic parallelism while preserving clinical intent. The generation process followed a structured workflow: selecting scenario descriptions from standardized mental health scales; defining critical words (CWs), markers, modifiers, time frames, and tense; generating broad context descriptions and four vignette sentences using GPT-4o; verifying quality; and, if necessary, regenerating and rewriting in third person for broader applicability. Thirty VGT-generated vignettes were compared with thirty human-authored vignettes produced by clinical experts, evaluated across ten criteria including vocabulary level, clinical relevance, naturalness, and logical structure. Ratings by three independent evaluators, unfamiliar with to the source of the vignettes, indicated that VGT outputs were comparable to human-written ones. VGT vignettes showed stronger control over lexical perplexity and structural consistency without compromising naturalness, plausibility, formality, or clinical suitability. In contrast, expert-authored vignettes demonstrated superior discriminability between expected and unexpected conditions and exhibited slightly higher levels of desirable unexpectedness.