GEO is grounded in academic research and systematic testing. This page collects the key studies, papers, and resources that inform GEO best practices.
Academic Research
Foundational Studies
GEO: Generative Engine Optimization
Aggarwal, et al. (2025)
This foundational research paper introduces the concept of Generative Engine Optimization and proposes a framework for understanding how content can be optimized for AI-powered search engines.
The study demonstrates that traditional SEO techniques have different effects when applied to generative engines, and that specific content optimization strategies can measurably improve visibility in AI responses.
Read on arXiv →Manipulating Large Language Models to Increase Product Visibility
Agarwal, Mandal, Neverkamp, Weld (2025) - Harvard University
This research from Harvard examines how content optimization strategies can influence LLM recommendations. The study explores how strategic content modifications affect product visibility when users ask AI systems for recommendations.
The paper demonstrates that specific content strategies can measurably impact whether and how products are recommended by large language models, providing empirical evidence for GEO effectiveness.
Read on arXiv →42A GEO Research
42A Research Team (2025)
42A conducts ongoing research into content optimization strategies for AI visibility, systematically testing tactics across major LLMs to identify which approaches deliver measurable improvements.
Key findings from 42A's research include:
- Statistics addition: +30-40% visibility improvement
- Source citations: +30-40% visibility improvement
- Keyword stuffing: ~10% visibility decrease
Contact 42A for access to full research findings.
Related Academic Work
Large Language Models and Search
Research on how LLMs process and retrieve information, and how they select sources for synthesized responses.
AI Citation Behavior
Studies examining how AI systems select, cite, and attribute sources when generating responses.
Content Quality Signals in AI
Research on what factors influence AI systems' assessment of content authority and trustworthiness.
Industry Research
Market Reports
Industry analysts have begun tracking the emergence of AI search and its implications for marketing:
- Gartner: Research on AI's impact on search and content discovery
- Forrester: Analysis of conversational AI adoption and marketing implications
- McKinsey: Reports on generative AI in marketing and customer engagement
Platform Insights
Understanding how each AI platform handles content and citations:
ChatGPT / OpenAI
OpenAI's documentation on how ChatGPT uses web browsing and retrieval to inform responses.
Claude / Anthropic
Anthropic's approach to truthfulness, citation, and source attribution in Claude's responses.
Perplexity
Perplexity's search-first approach and how it selects and cites sources.
Google Gemini
Google's AI Overviews and how Gemini integrates search results with AI responses.
Further Reading
Articles & Guides
- What is GEO? — Our comprehensive introduction to Generative Engine Optimization
- GEO Strategies — Research-backed tactics for improving AI visibility
- GEO vs SEO — Understanding how the two disciplines differ and overlap
Stay Updated
GEO is a rapidly evolving field. We update this resource page regularly as new research emerges. Key areas of ongoing research include:
- How AI model updates affect content visibility
- Cross-platform optimization strategies
- Measurement and attribution in AI search
- Industry-specific GEO best practices
Glossary
Key terms used in GEO research and practice:
- Generative Engine Optimization (GEO)
- The practice of optimizing content to improve visibility and prominence in AI-powered search engines and conversational assistants.
- AI Visibility
- The degree to which a brand or content is mentioned, cited, or recommended by AI systems in response to relevant queries.
- LLM (Large Language Model)
- The AI models that power conversational assistants like ChatGPT, Claude, and Gemini. These models are trained on large text datasets and generate human-like responses.
- Share of Voice (AI)
- The percentage of AI responses mentioning your brand compared to competitors for a given set of queries.
- Citation Quality
- How prominently and positively your brand is mentioned in AI responses (e.g., primary recommendation vs. alternative mention).
- Entity
- A distinct, identifiable thing (person, place, organization, concept) that AI systems can recognize and associate with attributes and relationships.
- Knowledge Graph
- A structured representation of entities and their relationships, used by search engines and AI systems to understand and connect information.
- Schema Markup
- Structured data added to web pages that helps search engines and AI systems understand the content's meaning and context.
- Retrieval-Augmented Generation (RAG)
- A technique where AI systems retrieve relevant information from external sources before generating a response, improving accuracy and enabling citations.
- Zero-Click Search
- Search queries that are answered directly in the search results or AI response, without the user clicking through to a website.