Ads are generated based on product info and consumer profiles. A discriminator evaluates probabilities of ads being effective at causing consumer engagement. A decoder extracts product info from generated ads. Based on the probabilities of ads being effective and similarity of extracted and source product info, generated ads are labeled as examples. The examples are used in training an improved ad generator. Ads may be visual and/or audio containing speech. Ads may even contain humor, as recognized by mismatches between source and decoded product info.