VQV Signal
Generative Engine Optimization (GEO) is usually assessed by how likely content is cited in a single AI-generated answer, but this approach overlooks the multi-turn nature of human-agent interactions. The paper proposes a trajectory-level framework to better evaluate GEO across multi-turn conversati...
Generative Engine Optimization (GEO) is usually assessed by how likely content is cited in a single AI-generated answer, but this approach overlooks the multi-turn nature of human-agent interactions. The paper proposes a trajectory-level framework to better evaluate GEO across multi-turn conversati...
AI-assisted summary based on the listed source.
Generative Engine Optimization (GEO) shapes content to increase its likelihood of being cited by answer engines built on retrieval-augmented large language models. GEO is typically evaluated as a single-turn property: for a fixed query, an evaluator measures a source's visibility in one answer. We argue that the...
Understanding GEO in the context of multi-turn interactions aligns evaluation with real-world human information-seeking behavior, potentially improving AI search relevance and user experience. This shift could lead to more effective optimization strategies for retrieval-augmented large language mod...
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