Generative Search Engines (GSEs), driven by advances in Large Language Models (LLMs) and Retrieval-Augmented Generation (RAG) technology, are revolutionizing information retrieval. Unlike traditional search engines that depend mainly on keyword matching and link algorithms, GSEs synthesize semantic data from multiple sources to deliver more nuanced and contextually relevant answers. Examples like BingChat and Perplexity. ai illustrate how integrating retrieval and generation yields coherent, meaningful responses. Despite their user experience benefits, GSEs challenge established Search Engine Optimization (SEO) methods, which have traditionally focused on optimizing content for transparent ranking factors such as keywords, metadata, and site structures within classic search engines. In contrast, GSEs function as black-box systems with complex, opaque algorithms for ranking and response generation. Their semantic and context-aware retrieval-generation process is not directly influenced by conventional SEO tactics, creating a disconnect where traditional optimization can reduce content visibility and impact in the generative search environment. To address this, a novel strategy called Role-Augmented Intent-Driven Generative Search Engine Optimization (G-SEO) has been developed. G-SEO centers on modeling user search intent through a reflective refinement process that accounts for diverse informational roles. Rather than focusing narrowly on keywords, it interprets the purpose and context behind queries, guiding content creators to refine materials aligning better with GSEs’ semantic processing. Evaluating optimization within GSEs presents challenges due to prior datasets' limited scope.

To counter this, G-SEO was tested on an expanded GEO dataset featuring diverse query variations that better mimic complex, real-world search behavior. Additionally, G-Eval 2. 0, a six-level evaluation rubric enhanced by large language models, offers a fine-grained, human-aligned assessment. This methodology captures both subjective user impressions and objective content visibility, ensuring improvements are meaningful and measurable from a user satisfaction perspective. Experiments with G-SEO demonstrate that leveraging search intent for content optimization significantly boosts visibility and relevance in GSE-generated results. Compared to baseline methods optimizing isolated content aspects, G-SEO consistently yields better outcomes in both subjective and objective metrics, highlighting the value of intent-driven, role-augmented approaches for navigating GSEs’ complex retrieval and synthesis mechanisms. These findings hold vital implications for content creators, marketers, and SEO professionals adapting to the evolving search landscape. As GSEs reshape how users find and engage with information, adopting intent-focused, role-aware optimization will be crucial for maintaining and enhancing digital presence. The novel methodologies introduced—including the enriched GEO dataset and advanced G-Eval 2. 0 rubric—also provide valuable tools for ongoing research and advancement in generative search optimization. In summary, the emergence of generative search engines signals a transformative shift in information retrieval, presenting both challenges and opportunities for SEO. The Role-Augmented Intent-Driven G-SEO framework offers an effective solution aligned with GSEs’ semantic and contextual dynamics by emphasizing search intent and supported by improved evaluation resources. As this field advances, continued innovation in optimization strategies and benchmarking standards will be essential to fully realize the transformative potential of generative search technology.