This working paper reviews and translates a broad array of academic research on "silicon
sampling"—using Large Language Models (LLMs) to simulate public opinion—and offers
guidance for practitioners, particularly those in communications and media industries,
conducting message testing and exploratory audience-feedback research. Findings show
LLMs are effective complements for preliminary tasks like refining surveys but are generally
not reliable substitutes for human respondents, especially in policy settings. The models
struggle to capture nuanced opinions and often stereotype groups due to training data bias
and internal safety filters. Therefore, the most prudent approach is a hybrid pipeline that
uses AI to improve research design while maintaining human samples as the gold standard
for data. As the technology evolves, practitioners must remain vigilant about these core
limitations. Responsible deployment requires transparency and robust validation of AI
findings against human benchmarks. Based on the translational literature review we
perform here, we offer a decision framework that can guide research integrity while
leveraging the benefits of AI.