What Generative Search Engines Like and How to Optimize Web Content
  Cooperatively
By employing large language models (LLMs) to retrieve documents and generate
natural language responses, Generative Engines, such as Google AI overview and
ChatGPT, provide significantly enhanced user experiences and have rapidly
become the new form of search. Their rapid adoption also drives the needs of
Generative Engine Optimization (GEO), as content providers are eager to gain
more traction from them. In this paper, we introduce AutoGEO, a framework to
automatically learn generative engine preferences when using retrieved contents
for response generation, and rewrite web contents for more such traction.
AutoGEO first prompts frontier LLMs to explain generative engine preferences
and extract meaningful preference rules from these explanations. Then it uses
preference rules as context engineering for AutoGEO_API, a
prompt-based GEO system, and as rule-based rewards to train
AutoGEO_Mini, a cost-effective GEO model. Experiments on the standard
GEO-Bench and two newly constructed benchmarks using real user queries
demonstrate the effectiveness of AutoGEO in enhancing content traction while
preserving search utility. Analyses confirm the learned rules' robustness and
abilities to capture unique preferences in variant domains, and AutoGEO
systems' ability to embed them in content optimization. The code is released at
https://github.com/cxcscmu/AutoGEO.
68eea08a510125ca000ccf59
Yujiang Wu
68eea08a510125ca000ccf5a
Shanshan Zhong
claimed_verified
2025-10-15T03:13:18.432Z
64311e8edd466752c73ab69a
/avatars/34f559399e57534ee9883029c8b91ccf.svg
Shanshan Zhong
user
zhongshsh
68eea08a510125ca000ccf5b
Yubin Kim
68eea08a510125ca000ccf5c
Chenyan Xiong
What Generative Search Engines Like and How to Optimize Web Content
  Cooperatively
By employing large language models (LLMs) to retrieve documents and generate
natural language responses, Generative Engines, such as Google AI overview and
ChatGPT, provide significantly enhanced user experiences and have rapidly
become the new form of search. Their rapid adoption also drives the needs of
Generative Engine Optimization (GEO), as content providers are eager to gain
more traction from them. In this paper, we introduce AutoGEO, a framework to
automatically learn generative engine preferences when using retrieved contents
for response generation, and rewrite web contents for more such traction.
AutoGEO first prompts frontier LLMs to explain generative engine preferences
and extract meaningful preference rules from these explanations. Then it uses
preference rules as context engineering for AutoGEO_API, a
prompt-based GEO system, and as rule-based rewards to train
AutoGEO_Mini, a cost-effective GEO model. Experiments on the standard
GEO-Bench and two newly constructed benchmarks using real user queries
demonstrate the effectiveness of AutoGEO in enhancing content traction while
preserving search utility. Analyses confirm the learned rules' robustness and
abilities to capture unique preferences in variant domains, and AutoGEO
systems' ability to embed them in content optimization. The code is released at
https://github.com/cxcscmu/AutoGEO.
AutoGEO, a framework for optimizing generative engines, learns and applies preference rules to enhance content traction and search utility using large language models.
large language models Generative Engines Generative Engine Optimization AutoGEO GEO-Bench prompt-based GEO system rule-based rewards content optimization