What Generative Search Engines Like and How to Optimize Web Content Cooperatively

2025-10-13

AI Summary

The rise of generative search engines (e.g., Google AI Overview) has spurred demand for Generative Engine Optimization (GEO), yet existing approaches lack interpretable modeling of model preference mechanisms and automated rule extraction. This paper proposes AutoGEO—a novel framework that, for the first time, automatically mines preference rules from large language model (LLM) explanations and integrates them into context engineering and reinforcement learning–based reward shaping for content-coordinated rewriting. AutoGEO comprises two variants: an efficient API-based version (AutoGEO$_ ext{API}$) and a lightweight variant (AutoGEO$_ ext{Mini}$). Evaluated on GEO-Bench and two real-world query benchmarks, AutoGEO significantly improves content traction (+28.7%) without compromising search utility. The learned rules demonstrate cross-domain robustness. AutoGEO establishes an end-to-end, interpretable, and generalizable optimization paradigm for GEO.

Technology Category

• Search and Optimization: Learning to Search
• Natural Language Processing: Prompt Engineering / Prompting
• Machine Learning: Learning Preferences or Rankings

Application Category

• Search and Retrieval-Augmented AI: Agentic search
• Web Mining and Content Analysis: Large pretrained models with web data
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions

Abstract

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$_ ext{API}$, a prompt-based GEO system, and as rule-based rewards to train AutoGEO$_ ext{Mini}$, 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.

Problem

• Automatically learning generative engine preferences for content optimization
• Rewriting web content to gain more traction from generative search engines
• Developing cost-effective models for Generative Engine Optimization (GEO)

Innovation

• Automatically learns generative engine preferences from LLM explanations
• Extracts preference rules for prompt-based and cost-effective optimization
• Rewrites web content to enhance traction while preserving utility