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Artificial intelligence optimization

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Artificial intelligence optimization (AIO) or AI optimization is a discipline concerned with improving the structure, clarity, and retrievability of digital content for large language models (LLMs) and other AI systems.[1][2][3] AIO is also known as Answer Engine Optimization (AEO), which targets AI-powered systems like ChatGPT, Perplexity and Google's AI Overviews that provide direct responses to user queries.[4][5][6][7][8][9][10][11] AI Optimization (AIO) builds on these insights by introducing formalized metrics and structures—such as the Trust Integrity Score (TIS)—to improve how content is embedded, retrieved, and interpreted by LLMs.[12][13][14][15][16][17][18][18][19][20]

See also

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References

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  1. ^ Huang, Sen; Yang, Kaixiang; Qi, Sheng; Wang, Rui (2024-10-01). "When large language model meets optimization". Swarm and Evolutionary Computation. 90 101663. arXiv:2405.10098. doi:10.1016/j.swevo.2024.101663. ISSN 2210-6502.
  2. ^ Hemmati, Atefeh; Bazikar, Fatemeh; Rahmani, Amir Masoud; Moosaei, Hossein. "A Systematic Review on Optimization Approaches for Transformer and Large Language Models". TechRxiv. doi:10.36227/techrxiv.173610898.84404151 (inactive 1 July 2025).{{cite journal}}: CS1 maint: DOI inactive as of July 2025 (link)
  3. ^ "From SEO to AIO: Artificial intelligence as audience". annenberg.usc.edu. Retrieved 2025-05-02.
  4. ^ Scott, Anthony (30 July 2025). "From SEO to AEO & GEO: How to Dominate Online Visibility in the Age of AI Search". NetQuall.
  5. ^ Fabled Sky Research (2022-12-09). "Artificial Intelligence Optimization (AIO) - A Probabilistic Framework for Content Structuring in LLM-Dominant Information Retrieval". Center for Open Science. Fabled Sky Research. doi:10.17605/OSF.IO/EBU3R.
  6. ^ Apoorav Sharma; Mr Prabhjot Dhiman (2025), The Impact of AI-Powered Search on SEO: The Emergence of Answer Engine Optimization, Unpublished, doi:10.13140/RG.2.2.20046.37446, retrieved 2025-04-16
  7. ^ "Measuring Goodhart's law". openai.com. 2024-02-14. Retrieved 2025-05-02.
  8. ^ "Understanding LLM Embeddings for Regression". Google DeepMind. 2025-04-24. Retrieved 2025-05-02.
  9. ^ "USER-LLM: Efficient LLM contextualization with user embeddings". research.google. Retrieved 2025-05-02.
  10. ^ Kelbert, Dr Julien Siebert, Patricia (2024-06-17). "Wie funktionieren LLMs? Ein Blick ins Innere großer Sprachmodelle - Blog des Fraunhofer IESE". Fraunhofer IESE (in German). Retrieved 2025-04-16.{{cite web}}: CS1 maint: multiple names: authors list (link)
  11. ^ Aggarwal, Pranjal; Murahari, Vishvak; Rajpurohit, Tanmay; Kalyan, Ashwin; Narasimhan, Karthik; Deshpande, Ameet (2024-08-24). "GEO: Generative Engine Optimization". Proceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining. KDD '24. New York, NY, USA: Association for Computing Machinery. pp. 5–16. arXiv:2311.09735. doi:10.1145/3637528.3671900. ISBN 979-8-4007-0490-1.
  12. ^ Bashir, A; Chen, RL; Delgado, M; Watson, JW; Hassan, Z; Ivanov, P; Srinivasan, T (2025-02-03). "Trust Integrity Score (TIS) as a Predictive Metric for AI Content Fidelity and Hallucination Minimization". National System for Geospatial Intelligence. doi:10.5281/zenodo.15330846.
  13. ^ "What is RAG? - Retrieval-Augmented Generation AI Explained - AWS". Amazon Web Services, Inc. Retrieved 2025-05-03.
  14. ^ Grytsai, Viktor. "AI Knowledge Management: Turning Internal Data into Answers". www.eteam.io. Retrieved 2025-05-03.
  15. ^ Meskó, Bertalan; Topol, Eric J. (2023-07-06). "The imperative for regulatory oversight of large language models (or generative AI) in healthcare". npj Digital Medicine. 6 (1): 120. doi:10.1038/s41746-023-00873-0. ISSN 2398-6352. PMC 10326069. PMID 37414860.
  16. ^ Klang, Eyal; Apakama, Donald; Abbott, Ethan E.; Vaid, Akhil; Lampert, Joshua; Sakhuja, Ankit; Freeman, Robert; Charney, Alexander W.; Reich, David; Kraft, Monica; Nadkarni, Girish N.; Glicksberg, Benjamin S. (2024-11-18). "A strategy for cost-effective large language model use at health system-scale". npj Digital Medicine. 7 (1): 320. doi:10.1038/s41746-024-01315-1. ISSN 2398-6352. PMC 11574261. PMID 39558090.
  17. ^ "AI on Trial: Legal Models Hallucinate in 1 out of 6 (or More) Benchmarking Queries | Stanford HAI". hai.stanford.edu. Retrieved 2025-05-03.
  18. ^ a b Mishra, Tanisha; Sutanto, Edward; Rossanti, Rini; Pant, Nayana; Ashraf, Anum; Raut, Akshay; Uwabareze, Germaine; Oluwatomiwa, Ajayi; Zeeshan, Bushra (2024-12-30). "Use of large language models as artificial intelligence tools in academic research and publishing among global clinical researchers". Scientific Reports. 14 (1): 31672. Bibcode:2024NatSR..1431672M. doi:10.1038/s41598-024-81370-6. ISSN 2045-2322. PMC 11685435. PMID 39738210.
  19. ^ Glickman, Mark; Zhang, Yi (2024-04-30). "AI and Generative AI for Research Discovery and Summarization". Harvard Data Science Review. 6 (2). arXiv:2401.06795. doi:10.1162/99608f92.7f9220ff. ISSN 2644-2353.
  20. ^ Palmer, Kathryn. "Publishers Embrace AI as Research Integrity Tool". Inside Higher Ed. Retrieved 2025-05-03.
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