As the rise of LLM-based search engines like Bard and Perplexity leads to robots directly outputting answers, it has become increasingly challenging for content creators to improve their websites through SEO. To assist content creators in better understanding how their content performs in generative engines and to provide strategies for optimizing this content to enhance its visibility and effectiveness within these engines, Princeton University and the Allen Institute of Technology have introduced GEO. GEO proposes a specialized impression metric tailored for generative engines. The principles of GEO include multimodal understanding, content synthesis, and semantic comprehension. By implementing the strategies proposed by GEO and participating in the GEO-BENCH benchmark tests, content creators can enhance the visibility and effectiveness of their websites and content within generative engines, thereby better meeting the search needs of users.