Draft:Generative Engine Optimization
Submission declined on 5 November 2025 by Aesurias (talk).
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| Submission declined on 2 September 2025 by Pythoncoder (talk). Your draft shows signs of having been generated by a large language model, such as ChatGPT. Their outputs usually have multiple issues that prevent them from meeting our guidelines on writing articles. These include: Declined by Pythoncoder 2 months ago.
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| Submission declined on 1 September 2025 by CNMall41 (talk). This submission is not adequately supported by reliable sources. Reliable sources are required so that information can be verified. If you need help with referencing, please see Referencing for beginners and Citing sources. Declined by CNMall41 2 months ago. | |
Comment: Likely notable, but not as written. Too technical and uses promotional links. Need reliable secondary sources. CNMall41 (talk) 23:20, 1 September 2025 (UTC)
Generative Engine Optimization (GEO) is a digital marketing practice that aims to increase the visibility of content within responses generated by large language models (LLMs) and AI-powered search tools.
The concept emerged alongside the development of generative AI platforms including ChatGPT, Claude, and Google's Search Generative Experience, which began incorporating AI-generated summaries into search results. GEO represents an adaptation of traditional search engine optimization (SEO) techniques to address how users interact with AI-generated content rather than traditional web search results.[1]
Background
[edit]Traditional SEO focuses on improving website rankings in search engine results pages to drive traffic to websites. GEO differs in that it targets inclusion within AI-generated text responses, where users may receive answers without clicking through to source websites.[2]
Research by Pew Research Center found that users clicked on search results in 8% of searches that included an AI summary, compared to 15% of searches without AI summaries.[3] This shift in user behavior has prompted marketing professionals to develop techniques for optimizing content specifically for AI-generated responses.
In 2024, Gartner predicted that search engine query volume would decline by 25% by 2026 due to the adoption of AI chatbots and virtual agents.[4]
Academic research
[edit]Academic researchers have developed formal frameworks for GEO. Gao et al. introduced a systematic approach to measuring content visibility in generative engines and proposed GEO-bench, a benchmark for evaluating optimization effectiveness.[5]
Aggarwal et al. presented research at the ACM SIGKDD Conference that demonstrated methods for domain-specific optimization and measured improvements in generative engine visibility.[6]
Techniques and implementation
[edit]GEO approaches generally focus on making information easy for AI systems to understand and reuse in their answers. Unlike traditional search, where ranking pages for clicks is the main goal, GEO emphasises clarity, context, and credibility so that model-generated summaries are more likely to include a publisher’s material. Industry and research sources note that language models respond well to content that is clearly structured, uses plain language, and cites reliable sources.[7][8]
Studies and practitioner guidance describe practical steps such as:
- organising pages around the specific questions users ask and providing direct, concise answers;
- supporting claims with citations, statistics and primary sources where appropriate;
- using headings, summaries and schema/metadata so key facts are machine-readable;[9][10][11]
Some AI systems incorporate real-time web search, which creates overlap between traditional SEO and GEO. At the same time, many language models are updated only at intervals rather than continuously, so new information may not be reflected until a model is retrained. Practitioners therefore distinguish between tactics aimed at “live” systems and those intended to remain useful to periodically updated models.[12]
Impact on advertising
[edit]Research has examined how GEO affects paid search advertising. A 2024 study identified two competing effects of AI-generated summaries on website traffic: a cannibalization effect where AI answers reduce direct traffic, and a competition effect where websites compete more intensively for inclusion in AI responses.[13]
The study found that high-performing websites experienced greater traffic cannibalization, while less efficient competitors benefited from increased competition for AI inclusion. Gartner has also predicted that widespread adoption of AI overviews will reshape search advertising revenue models.[4]
Regulatory considerations
[edit]The Artificial Intelligence Act in the European Union includes transparency and attribution requirements that may affect GEO practices. In the United States, the Federal Trade Commission has issued guidance regarding undisclosed sponsored content in AI-generated responses.[14]
Criticism
[edit]Some analysts and practitioners question whether Generative Engine Optimization constitutes a distinct discipline, arguing that many of its techniques overlap with established SEO practices such as structured content, authoritative sourcing, and metadata optimisation. Trade press outlets report that there is no consensus within the SEO community, with some professionals describing GEO as "SEO rebranded" or a marketing buzzword rather than a novel methodology.[15][16]
Other commentators have raised concerns about GEO’s potential to amplify misinformation or manipulate information sources. Publications including Wired caution that optimisation for AI responses could prioritise visibility over accuracy and increase the risk of misleading outputs.[17]
Related concepts
[edit]GEO is related to Answer Engine Optimization (AEO), which focuses on optimizing content for voice search and featured snippets, and conversational SEO techniques that target natural language queries.
See also
[edit]- Search engine optimization
- Answer Engine Optimization
- Large language model
- Prompt engineering
- Zero-click search
References
[edit]- ^ "What is GEO?". Startups.co.uk. 11 July 2024. Archived from the original on 29 July 2025. Retrieved 2 September 2025.
- ^ "Generative Engine Optimization: The Future of Search Is Here". Forbes Agency Council. 2 January 2025. Archived from the original on 26 August 2025. Retrieved 2 September 2025.
- ^ "Google users are less likely to click on links when an AI summary appears in the results". Pew Research Center. 22 July 2025. Retrieved 2 September 2025.
- ^ a b "Gartner Predicts Search Engine Volume Will Drop 25% by 2026, Due to AI Chatbots and Other Virtual Agents". Gartner. 19 February 2024. Archived from the original on 31 August 2025. Retrieved 2 September 2025.
- ^ Gao, X.; Raj, R.; Zhang, Y. et al. (2024). "Generative Engine Optimization: Black-Box Optimization for Content Visibility in Generative Engines". Proceedings of the ACM on Web Conference 2024. Association for Computing Machinery. doi:10.1145/3637528.3671900.
- ^ Aggarwal, P.; Murahari, V.; Rajpurohit, T.; Kalyan, A.; Narasimhan, K.; Deshpande, A. (2024). "GEO: Generative Engine Optimization". Proceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD '24). Association for Computing Machinery. pp. 1–12. doi:10.1145/3637528.3671900. Archived from the original on 19 June 2025. Retrieved 2 September 2025.
{{cite conference}}: CS1 maint: multiple names: authors list (link) - ^ "Wie funktionieren LLMs?". Fraunhofer IESE. 2024. Retrieved 2 September 2025.
- ^ Yao, Shunyu; Yu, Dian; Zhao, Jeffrey; Shafran, Izhak; Griffiths, Thomas L.; Cao, Yuan; Narasimhan, Karthik (2023). "Tree of Thoughts: Deliberate Problem Solving with Large Language Models". arXiv:2305.09612 [cs.CL].
- ^ "Generative Engine Optimization: A Marketer's Guide". HubSpot. 2024. Archived from the original on 12 August 2025. Retrieved 2 September 2025.
- ^ "How to Stay Relevant When AI Starts Writing the Internet Without You". Forbes Technology Council. 8 April 2025. Archived from the original on 23 July 2025. Retrieved 2 September 2025.
- ^ "Generative Engine Optimization: Emerging Techniques". Search Engine Land. 2024. Retrieved 2 September 2025.
- ^ "GEO over SEO". Andreessen Horowitz. 2024. Archived from the original on 29 August 2025. Retrieved 2 September 2025.
- ^ Vajratiya, Vajrobol; Aggarwal, Nitisha; Jain Saxena, Geetika; Singh, Sanjeev; Pundir, Amit (2024). "Transforming SEO in the Era of Generative AI: Challenges, Opportunities, and Future Prospects". Revolutionizing the AI-Digital Landscape. Taylor & Francis. pp. 86–100. doi:10.4324/9781032688305-6. ISBN 978-1-032-68830-5. Retrieved 2 September 2025.
{{cite book}}: CS1 maint: multiple names: authors list (link) - ^ "The New Rules for Brand Visibility in Generative Search". CMSWire. 2025. Retrieved 2 September 2025.
- ^ "GEO and SEO: Convergence, divergence or something in between?". Search Engine Land. 8 September 2025. Retrieved 23 September 2025.
- ^ "SEOs can't agree on what to call AI search optimization". Search Engine Land. 11 September 2025. Retrieved 23 September 2025.
- ^ "Optimize for Authentic Voices in a Sea of AI Search Spam". Wired. 2024. Retrieved 2 September 2025.
- Promotional tone, editorializing and other words to watch
- Vague, generic, and speculative statements extrapolated from similar subjects
- Essay-like writing
- Hallucinations (plausible-sounding, but false information) and non-existent references
- Close paraphrasing
Please address these issues. The best way is usually to read reliable sources and summarize them, instead of using a large language model. See our help page on large language models.