In September 2017 the Very Large Array (VLA) began the first three epochs of observations for the Very Large Array Sky Survey (VLASS). Each epoch of the survey is split into two observing cycles with 6 cycles total to be completed over 7 years. During each epoch the VLA will survey ~80% of the sky with declination > -40° in full polarization between 2-4 GHz and generate 35,500 sets of products, each covering ~1 square degree of sky. To ensure the survey meets its science goals a Quality Assurance (QA) workflow was developed whereby each product was manually inspected before being released to the community. However, this manual workflow has been found to be prone to random human error and the pace depended on the efficiency of those performing the QA. We have sought to decrease the time between observation and the delivery of the image product to the community and to standardize the QA of each product by developing an automated QA workflow. In doing so we have transcribed the manual QA ruleset for Quick Look (QL) image products into a python code that employs heuristic methods and a neural network to identify image products that contain unwanted artifacts. After applying our new automated QA workflow to QL images produced during the first half of the third epoch of VLASS we present the results of our automation. We show that compared to previous observing cycles we have significantly increased the efficiency of QL image QA through our automation by decreasing delivery time to the community as well as other overhead costs of manually performing QA.