Many aging cohort studies have collected data on participants' job titles, yet these job titles were seldom analyzed within the cognitive aging context despite their relevance to neurocognition, due to difficulties in analyzing these job titles quantitatively. While it is possible to rate these jobs' occupational complexity (OC) using job classification systems, this can be somewhat labor-intensive and prone to human errors. To this end, we demonstrate a novel and simple method to extract OC ratings from job titles using ChatGPT. Then, we showcased the utility of these ratings in predicting cognitive and structural brain outcomes, especially compared to other socioeconomic status (SES) indicators. Community-dwelling older adults (N = 238, age