GEO Lab

Through controlled variable experiments, systematically study the citation behavior patterns of mainstream AI platforms when generating brand recommendation responses. Analyze which types of information sources AI tends to cite (official websites, third-party reviews, user reviews, encyclopedia entries, etc.), and the distribution characteristics of citation formats and frequencies. The experiment covers four major platforms: ChatGPT, DeepSeek, Doubao, and Kimi, providing data-driven optimization directions for brand content building.

Design multiple sets of controlled prompts to test how different questioning approaches, context settings, and constraints affect AI brand recommendation results. The study found: contextual information in prompts such as scenario descriptions, budget ranges, and target user groups significantly changes the brand distribution of recommendations; open-ended questions produce more diverse brand recommendations than constrained questions; different platforms show明显 differences in prompt sensitivity. The experimental results provide empirical evidence for brand prompt strategy optimization.