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.