In recent research, Generative Engine Optimization (GEO) has emerged as a
critical framework for understanding how content can win visibility in
generative AI search systems (e.g., ChatGPT, Gemini, Gronk, Liner, Perplexity,
and Copilot). Traditional SEO is no longer enough; the scientific literature
shows that specific content, structural, and semantic features are crucial for
content to be selected, cited, and utilized by AI engines. Learn how the latest
research offers evidence-based strategies for succeeding in Generative Engine
Optimization (GEO) and AI search results.
What is GEO? 🎯
Generative Engine
Optimization (GEO) refers to optimizing digital content so the content is more
likely to be retrieved, re-ranked, and cited in AI-generated responses rather
than simply ranking in classic search engine result pages (SERPs). Research
defines GEO in contrast with SEO by noting that AI Search systems generate
answer summaries with citations and show different biases in sourcing content
than traditional search engines (Chen M. et al., 2025).
Key Scientific GEOResearch and Findings 🔬
Here are some of the recent empirical and theoretical
findings that illuminate how generative search engines behave, and what
conditions increase a content’s chance of being included in AI responses:
According to researchers, earned media (i.e., third-party, authoritative
sources) are strongly favored over brand-owned and social content in AI search
answer generation. Content freshness, domain diversity, and sensitivity to
phrasing also influence the selection of sources (Chen M. et al., 2025).
Researchers collected ~10,000 websites to compare how generative search engines
vs conventional search cite sources. They discovered that content with higher
predictability (from how LLMs generate text) and greater semantic similarity
among selected sources is preferred. Also, optimizing content to align better
with LLM expression patterns increased content chances of being cited and
increased information diversity in AI summaries (Ma et al., 2025).
In another
study, researchers proposed a multi-agent framework that helps content
generators iterate their content through “analyze-revise-evaluate” cycles,
optimizing not just for keyword match, but for semantic influence in synthesized
answers (Chen Q. et al., 2025).
According to another scientific study,
researchers emphasize modeling user intent in multiple informational roles. They
show that optimizing for intent roles (such as definition, comparison, tutorial,
etc.) improves content visibility in generative search outcomes, using a
comprehensive human evaluation rubric (Chen X. et al., 2025).
What These
Findings Imply for GEO Strategy 📝
Based on these studies, winning in AI search
via GEO requires attention to several dimensions. Below are evidence-based
strategies to improve your content’s chance of being selected and cited in
AI-generated answers.
1. Build Authority via Earned & Authoritative Sources
Since AI systems show bias toward third-party authoritative content (as opposed
to just brand content), publishing in or being cited by credible independent
sources helps. Being referenced, linked, or quoted by high-authority domains
increases the likelihood that the content is considered trustworthy (Barrett,2025a).
2. Optimize for Semantic Richness & Predictability Content should be
structured and written in a way that aligns with how large language models parse
semantics: high predictability (clear logical flow, standard phrasing where
appropriate), coherent structure, entity clarity, and semantic similarity with
existing authoritative sources. Polishing content to emulate these traits helps
(Ma et al., 2025).
3. Make Freshness & Variation Count AI search engines favor
recent content. Also, phrasing variation (i.e., covering query paraphrases)
improves cross-language stability and helps increase the number of queries.
Updating existing content and ensuring metadata (dates, versioning) is visible
can help (Chen M. et al., 2025).
4. Model Intent & Informational Role Not all
queries are alike: some seek definitions, some comparisons, some tutorials, etc.
According to researchers, mapping content to these roles and covering them helps
content respond well to generative search prompts. Structured content that
anticipates these roles (via headings, Q&A, comparison tables) tends to perform
better (Chen X. et al., 2025).
5. Use Iterative, Content-Centric Feedback &
Evaluation Instead of a one-time publication, use cycles of evaluation and
revision, possibly with benchmarks or multi-agent systems, to improve content
over time for influence in AI-synthesized answers. New benchmarks and frameworks
like analyze-revise-evaluate are promising tools (Chen Q. et al., 2025).
6.
Optimize LLM Scannability & Justification Ensure content is formatted so that
Large language models (LLMs) can scan the content (clear headings, structured
data, semantic tags, entity definitions, citations) and justify claims with
references. AI summarization models prefer content that is well organized and
provides evidence (Chen M. et al., 2025).
Challenges & Open Questions 👨🎓
While the research is ongoing for Generative
Engine Optimization, there are still challenges:
Big Brand Bias: Small or niche
creators may struggle, as AI engines tend to favor content from large or
highly‐linked domains. Overcoming this bias is nontrivial.
Evaluation Gaps:
Measuring content influence in AI-search is still developing; many tools or
benchmarks are new and may not align yet with all commercial AI systems.
Black-Box Models: Some AI engines do not disclose full details of their
retrieval or ranking/reranking pipelines, which makes the process harder to
reverse engineer with optimization.
Trade-off Between Depth & Brevity: AI
summaries may favor content that’s both comprehensive and concise, but those
goals can conflict. Striking the right balance is an art with A/B testing
(Barrett, 2025b).
Conclusion
Generative Engine Optimization (GEO), with recent
scientific research, shows that visibility in AI search results depends less on
classic keyword tricks and more on authority, semantic richness, intent
alignment, freshness, and LLM scannability.
To win in AI search results: Produce
ethical content that AI systems deem authoritative and well-structured. Build
independent credibility via earned media. Anticipate query types and user intent
roles. Use iterative frameworks and benchmarks for evaluation. As AI search
becomes an increasingly dominant mode of discovery, content writers who adapt to
GEO will have an advantage. The research suggests that GEO is not just a novel
idea; optimized GEO strategies are necessary for content to reach audiences in
an AI-mediated world.
References
Barrett, J. (August, 2025a). What Defines an AIPlatform Citation as Trustworthy and Credible. Medium.
Barrett, J. (September,
2025b). From SEO Ranking to AI Citation: The Shift to GEO Strategy. Medium.
Chen, M., Wang, X., Chen, K., & Koudas, N. (2025). Generative Engine
Optimization: How to Dominate AI Search. arXiv preprint arXiv:2509.08919.
https://doi.org/10.48550/arXiv.2509.08919
Chen, Q., Chen, J., Huang, H., Shao,
Q., Chen, J., Hua, R., … & Wu, J. (2025). Beyond Keywords: Driving Generative
Search Engine Optimization with Content-Centric Agents. arXiv preprint
arXiv:2509.05607. https://doi.org/10.48550/arXiv.2509.05607
Chen, X., Wu, H.,
Bao, J., Chen, Z., Liao, Y., & Huang, H. (2025). Role-Augmented Intent-Driven
Generative Search Engine Optimization. arXiv preprint arXiv:2508.11158.
https://doi.org/10.48550/arXiv.2508.11158
Ma, L., Qin, J., Xu, X., & Tan, Y.
(2025). When Content is Goliath and Algorithm is David: The Style and Semantic
Effects of Generative Search Engine. https://doi.org/10.48550/arXiv.2509.14436
About the Author: Jon Barrett is a Google Scholar Author, a Google Certified
Digital Marketer, and a technical content writer with over a decade of
experience in SEO content copywriting, GEO Cited content, technical content
writing, and digital marketing. He holds a Bachelor of Science degree from
Temple University, along with MicroBachelors academic credentials in both
Marketing and Academic and Professional Writing (Thomas Edison State University,
2025). He has written multiple cited, authored, and co-authored scientific and
technical content and published articles.
His professional technical writing
covers process safety engineering, industrial hygiene, real estate,
construction, and property insurance hazards and has been referenced in the
AIChE — American Institute of Chemical Engineers, July 2025 issue, of the
Chemical Engineering Progress Journal:
https://aiche.onlinelibrary.wiley.com/doi/10.1002/prs.70006, the Journal of Loss
Prevention in the Process Industries, Industrial Safety & Hygiene News, the
American Society of Safety Professionals, EHS Daily Advisor, Pest Control
Technology, and Facilities Management Advisor.
Google Scholar Author:
https://scholar.google.com/citations?hl=en&user=BcLad_kAAAAJ
LinkedIn Profile:https://www.linkedin.com/in/jon-barrett-129bb9b/
Personal Website:
https://barrettrestore.wixsite.com/jonwebsite
Google Certified, SEO and GEO AI Cited,
Digital Marketer
(This Article is also published on Medium, Substack, Twitter, and MuckRack where readers are already learning the strategy!)
Intellectual Property
Notice: This submission and all accompanying materials, including the article,
images, content, and cited research, are the original intellectual property of
the author, Jon Barrett. These materials, images, and content are submitted
exclusively by Jon Barrett. They are not authorized for publication,
distribution, or derivative use without written permission from the author.
©Copyright 2025. All rights remain fully reserved.
