Generative Engine Optimization in digital repositories: optimizing visibility for generative AI
DOI:
https://doi.org/10.3145/infonomy.25.034Keywords:
GEO, Generative Engine Optimization, Digital repositories, Generative engines, Generative AI, Academic SEO, Academic repositories, Optimization, Visibility, Generative artificial intelligence, LLM, Reference RateAbstract
The rapid growth of generative Artificial Intelligence (AI) and large-scale language models (LLMs) has transformed the way information is searched and retrieved on the web. It has shifted from traditional keyword matching and list-based results with site links to generating synthesized, conversational responses. This change presents a significant challenge to the traffic of academic digital repositories. In response, Generative Engine Optimization (GEO) emerges as a potential solution. Current SEO strategies are inadequate for ensuring that new generative engines effectively reference content. This article aims to present the first GEO strategic guidelines for institutional repositories to enhance their visibility. The methodology develops a theoretical framework that compares different search methods and adapts industry GEO strategies to repository management. Key strategies, including technical optimization, semantic structuring, metadata quality, content enhancement, interoperability, multilingual support, and new metric exploration, are identified and proposed. Ultimately, it is argued that a balanced adoption of GEO can increase the impact of repositories while maintaining their mission as user-focused public services that provide open access to scientific knowledge in the AI era.References
Aggarwal, Pranjal; Murahari, Vishvak; Rajpurohit, Tanmay; Kalyan, Ashwin; Narasimhan, Karthik; Deshpande, Ameet (2024). GEO: Generative Engine Optimization. arXiv:2311.09735v3. https://doi.org/10.48550/arXiv.2311.09735
Amer, Eslam; Elboghdadly, Tamer (2024). The end of the search engine era and the rise of Generative AI: A paradigm shift in information retrieval. In: 2024 International Mobile, Intelligent, and Ubiquitous Computing Conference (MIUCC). https://doi.org/10.1109/MIUCC62295.2024.10783559
AppLabx (2025). How to optimize content for generative AI: A Step-by-step GEO checklist. AppLabx - Digital Marketing and Web Agency. https://blog.applabx.com/how-to-optimize-content-for-generative-ai-a-step-by-step-geo-checklist
Bommasani, Rishi; Hudson, Drew A.; Adeli, Ehsan; Altman, Russ; Arora, Simran; Arx, Sydney von; Bernstein, Michael S.; Bohg, Jeannette; Bosselut, Antoine; Brunskill, Emma; Brynjolfsson, Erik; Buch, Shyamal; Card, Dallas; Castellon, Rodrigo; Chatterji, Niladri; Chen, Annie; Creel, Kathleen; Davis, Jared Quincy; Demszky, Dora; Donahue, Chris; Doumbouya, Moussa; Durmus, Esin; Ermon, Stefano; Etchemendy, John; Ethayarajh, Kawin; Fei-Fei, Li; Finn, Chelsea; Gale, Trevor; Gillespie, Lauren; Goel, Karan; Goodman, Noah; Grossman, Shelby; Guha, Neel; Hashimoto, Tatsunori; Henderson, Peter; Hewitt, John; Ho, Daniel E.; Hong, Jenny; Hsu, Kyle; Huang, Jing; Icard, Thomas; Jain, Saahil; Jurafsky, Dan; Kalluri, Pratyusha; Karamcheti, Siddharth; Keeling, Geoff; Khani, Fereshte; Khattab, Omar; Koh, Pang Wei; Krass, Mark; Krishna, Ranjay; Kuditipudi, Rohith; Kumar, Ananya; Ladhak, Faisal; Lee, Mina; Lee, Tony; Leskovec, Jure; Levent, Isabelle; Li, Xiang Lisa; Li, Xuechen; Ma, Tengyu; Malik, Ali; Manning, Christopher D.; Mirchandani, Suvir; Mitchell, Eric; Munyikwa, Zanele; Nair, Suraj; Narayan, Avanika; Narayanan, Deepak; Newman, Ben; Nie, Allen; Niebles, Juan Carlos; Nilforoshan, Hamed; Nyarko, Julian; Ogut, Giray; Orr, Laurel; Papadimitriou, Isabel; Park, Joon Sung; Piech, Chris; Portelance, Eva; Potts, Christopher; Raghunathan, Aditi; Reich, Rob; Ren, Hongyu; Rong, Frieda; Roohani, Yusuf; Ruiz, Camilo; Ryan, Jack; Ré, Christopher; Sadigh, Dorsa; Sagawa, Shiori; Santhanam, Keshav; Shih, Andy; Srinivasan, Krishnan; Tamkin, Alex; Taori, Rohan; Thomas, Armin W.; Tramèr, Florian; Wang, Rose E.; Wang, William; Wu, Bohan; Wu, Jiajun; Wu, Yuhuai; Xie, Sang Michael; Yasunaga, Michihiro; You, Jiaxuan; Zaharia, Matei; Zhang, Michael; Zhang, Tianyi; Zhang, Xikun; Zhang, Yuhui; Zheng, Lucia; Zhou, Kaitlyn; Liang, Percy (2022). On the opportunities and risks of foundation models. https://doi.org/10.48550/arXiv.2108.07258
Buitrago-Ciro, Jairo; Morales-Campos, Estela; Villamizar-Romero, César-Leonardo (2025). ¿Cómo está transformando la inteligencia artificial la comunicación científica? Desafíos, oportunidades y el papel de los actores involucrados: una revisión de alcance. Investigación Bibliotecológica: archivonomía, bibliotecología e información, v. 39, n. 104. https://doi.org/10.22201/iibi.24488321xe.2025.104.59032
Calla Creative (2025). How to write FAQ sections that actually get picked up by AI search. https://callacreative.com/blog/how-to-write-faq-sections-that-actually-get-picked-up-by-ai-search
Castro, Leyla J.; Neumann, Steffen; Schneider, Gabriel (2024). SchemaOrg and JSON-LD for rich metadata integration in the NFDI. https://doi.org/10.5281/zenodo.10830175
Chen, Mahe; Wang, Xiaoxuan; Chen, Kaiwen; Koudas, Nick (2025). Generative Engine Optimization: How to dominate AI search. https://doi.org/10.48550/arXiv.2509.08919
COAR (2023). Good practice advice for managing multilingual and non-English language content in repositories. https://doi.org/10.5281/zenodo.10053918
COAR (2025). Open repositories are being profoundly impacted by AI bots and other crawlers: Report from a COAR Survey. https://coar-repositories.org/news-updates/open-repositories-are-being-profoundly-impacted-by-ai-bots-and-other-crawlers-results-of-a-coar-survey
Daniels, Chris (2025). From SEO to GEO: How agencies are navigating LLM-driven search | Analysis. Campaign Asia. https://www.campaignasia.com/article/from-seo-to-geo-how-agencies-are-navigating-llm-driven-search/501593
Dony, Christophe; Kuchma, Iryna; Ševkušić, Milica (2024). Dealing with multilingualism and non-English content in open repositories: Challenges and perspectives. The journal of electronic publishing, v. 27, n. 1. https://doi.org/10.3998/jep.5455
Farnel, Sharon (2023). Understanding repository functionality and structure. In:
Discoverability in digital repositories, 1st edition, Routledge. http://doi.org/10.4324/9781003216438-4
Font-Julián, Cristina I.; Orduña-Malea, Enrique; Codina, Lluís (2024). ChatGPT Search as a tool for scholarly tasks: evolution or devolution?. Infonomy, v. 2, n. 5. https://doi.org/10.3145/infonomy.24.059
Google Cloud (2025). Prácticas recomendadas para Cloud Storage. https://cloud.google.com/storage/docs/best-practices?hl=es-419
Gozalo-Brizuela, Roberto; Garrido-Merchán, Eduardo C. (2023). A survey of Generative AI Applications. https://doi.org/10.48550/arXiv.2306.02781
Hall, Brody (2025). What is Reference Rate in AI Search? Loganix. https://loganix.com/reference-rate
Hersh, William (2024). Search still matters: information retrieval in the era of generative AI. Journal of the American Medical Informatics Association : JAMIA, v. 31, n. 9. https://doi.org/10.1093/jamia/ocae014
INSIDEA (2025). How can you optimize site speed to improve AI engine indexing and UX?. https://insidea.com/blog/seo/aieo/optimize-site-speed-to-improve-ai-engine-indexing-and-ux
Handley, Rachel (2025). We studied the impact of AI search on SEO traffic. Here’s what we learned. Semrush Blog. https://www.semrush.com/blog/ai-search-seo-traffic-study
Kaiser, Carolin; Kaiser, Jakob; Schallner, Rene; Schneider, Sabrina (2025). A new era of online search? A large-scale study of user behavior and personal preferences during practical search tasks with generative AI versus traditional search engines. En: Proceedings of the extended abstracts of the CHI conference on human factors in computing systems. https://doi.org/10.1145/3706599.3720123
Kelly, Brenna (2025). Investigating ChatGPT Search: Insights from 80 million clickstream records. Semrush Blog. https://www.semrush.com/blog/chatgpt-search-insights/
Law, Ryan; Guan, Xibeijia (2025). New study: AI assistants prefer to cite “fresher” content (17 million citations analyzed). SEO Blog by Ahrefs. https://ahrefs.com/blog/do-ai-assistants-prefer-to-cite-fresh-content/
Lewis, Patrick; Perez, Ethan; Piktus, Aleksandra; Petroni, Fabio; Karpukhin, Vladimir; Goyal, Naman; Küttler, Heinrich; Lewis, Mike; Yih, Wen-tau; Rocktäschel, Tim; Riedel, Sebastian; Kiela, Douwe (2021). Retrieval-augmented generation for knowledge-intensive NLP Tasks. https://doi.org/10.48550/arXiv.2005.11401
Oxford College of Marketing (2025). From SEO to GEO: Why marketers must evolve with the future of search. https://blog.oxfordcollegeofmarketing.com/2025/06/10/from-seo-to-geo-why-marketers-must-evolve-with-the-future-of-search/
McKenzie, Leigh; Ofei, Michael (2025). LLM Visibility: The SEO metric no one Is reporting on (yet). https://backlinko.com/llm-visibility
OpenAIRE (2018). OpenAIRE guidelines and application profile for repository managers and publication platforms 4.0: More detail - More connectivity. https://www.openaire.eu/?view=article&id=2173&catid=61
Orduña-Malea, Enrique; Cabezas-Clavijo, Álvaro (2023). ChatGPT and the potential growing of ghost bibliographic references. Scientometrics, v. 128, n. 9. https://doi.org/10.1007/s11192-023-04804-4
Park, Lehm (2025). Structured data in the AI search era. https://www.brightedge.com/blog/structured-data-ai-search-era
Pekala, Shayna (2018). Microdata in the IR: A low-barrier approach to enhancing discovery of institutional repository materials in Google. The Code4Lib Journal, n. 39. https://journal.code4lib.org/articles/13191?utm_source=chatgpt.com
Pol, Tushar (2025). What Is LLMs.txt & should you use it? https://www.semrush.com/blog/llms-txt/
Reyes-Lillo, Danilo; Morales-Vargas, Alejandro; Rovira, Cristòfol (2025a). Visibility, discoverability, findability, search engine optimization (SEO) and Academic SEO in digital repositories: A scoping review. BiD, v. June, n. 54. https://doi.org/10.1344/BID2025.54.06
Reyes-Lillo, Danilo; Pastor-Ramon, Elena (2024). Use of handle in institutional repositories and its relationship with alternative metrics: A case study in Spanish-speaking America. Hipertext.Net, 29, 211–222. https://doi.org/10.31009/hipertext.net.2024.i29.17
Reyes-Lillo, Danilo; Rovira, Cristòfol; Morales-Vargas, Alejandro (2025b). Factores para aumentar la visibilidad en repositorios digitales: metadatos, interoperabilidad, identificadores persistentes y optimización SEO-GEO. En J. Guallar; M. Vállez; A. Ventura-Cisquella (Coords). Comunicación digital. Tendencias y buenas prácticas (pp. 126-141). Ediciones Profesionales de la Información. https://doi.org/10.3145/cuvicom.09.esp
Sain, Ankit (2025). How to structure content for AI discovery: FAQs, schema & conversational format. https://whitebunnie.com/blog/how-to-structure-content-for-ai-discovery-faqs-schema-conversational-format
SEOJuice (2025). Reference rate - Generative SEO metric explained - Generative Engine Optimization definition. https://seojuice.io/glossary/geo/citation/reference-rate
Shelby, Carolyn (2025). How LLMs interpret content: How to structure information for AI search. Search Engine Journal. https://www.searchenginejournal.com/how-llms-interpret-content-structure-information-for-ai-search/544308
Solatorio, Aivin; Dupriez, Olivier (2024). Efficient metadata enhancement with AI for better data discoverability. https://blogs.worldbank.org/en/opendata/efficient-metadata-enhancement-with-ai-for-better-data-discovera
SocialChampsVISIBLETM (2025). How to build citation-worthy content that AI
models prefer. VisibleTM. https://govisible.ai/blog/how-to-build-citation-worthy-content-that-ai-models-prefer
Tapfuma, Mass Masona; Hoskins, Ruth Geraldine (2022). Adoption of institutional repositories towards realization of digital libraries: The Southern African perspective. In: T. Masenya (Ed.), Innovative technologies for enhancing knowledge access in academic libraries (pp. 156-175). IGI Global Scientific Publishing. https://doi.org/10.4018/978-1-6684-3364-5.ch010
Venkit, Pranav Narayanan; Laban, Philippe; Zhou, Yilun; Mao, Yixin; Wu, Chien-Sheng (2024). Search engines in an AI era: The false promise of factual and verifiable source-cited responses. https://doi.org/10.48550/arXiv.2410.22349
Yan, Tony (2025). Schema markup best practices: Structured data that boosts AI citation & SEO. https://quickcreator.io/blog/schema-markup-best-practices-structured-data-ai-seo
Zavalina, Oksana L.; Burke, Mary (2023). User searching in digital repositories. In: Discoverability in digital repositories, Routledge. http://doi.org/10.4324/9781003216438-7
Zhang, Alex (2025). Information retrieval in the age of Generative AI: A mismatch that matters. Legal reference services quarterly. https://doi.org/10.1080/0270319X.2025.2536920
Downloads
Published
How to Cite
Downloads
Dimensions
Issue
Section
License
Copyright (c) 2025 Danilo Reyes-Lillo, Alejandro Morales-Vargas, Cristòfol Rovira
This work is licensed under a Creative Commons Attribution 4.0 International License.
