[1] McCarthy, J. (2004). Basic Questions. Stanford University. http://www-formal.stanford.edu/jmc/
[2] Feuerriegel, S., Hartmann, J., Janiesch, C., et al. (2024). Generative AI. Business & Information Systems
Engineering, 66, 111–126. https://doi.org/10.1007/s12599-023-00834-7
[3] Goldman Sachs (2023). The Potentially Large Effects of Artificial Intelligence on Economic Growth. (Industry
report, cited in Feuerriegel et al., 2024).
[4] Farquhar, S., Kossen, J., Kuhn, L., et al. (2024). Detecting hallucinations in large language models using semantic
entropy.Nature, 630, 625–630. https://doi.org/10.1038/s41586-024-07421-0
[5] Menshawy, A., Nawaz, Z., & Fahmy, M. (2024). Navigating challenges and technical debt in large language models
deployment. Proceedings of the 4th Workshop on Machine Learning and Systems (EuroMLSys ’24), 192–199.
https://doi.org/10.1145/3642970.3655840
[6] Pahune, S., & Akhtar, Z. (2025). Transitioning from MLOps to LLMOps: Navigating the unique challenges of large
language models. Information, 16(2), 87. https://www.mdpi.com/2078-2489/16/2/87
[7] Diaz-De-Arcaya, J., & López-De-Armentia, J. (2024). Large language model operations (LLMOps): Definition,
challenges, and lifecycle management. IEEE Conference Proceedings. https://ieeexplore.ieee.org/document/10612341
[8]
Joshi, S. (2025). LLMOps, AgentOps, and MLOps for Generative AI: A Comprehensive Review.
https://philpapers.org/archive/JOSLAA-3.pdf
[9] Özer, F. (2025). Systematic Technical Survey on LLMOps: Lifecycle, Tools, Challenges, and Emerging Practices.
https://erepo.uef.fi/bitstreams/4212c75f-1822-4648-aa2c-7c55c55d86c2/download
[10] Radenkovic, B., & Prokhorov, S. (2025). Application of Large Language Models in Software Development: Review
of the Current State and Development Perspectives. https://ieeexplore.ieee.org/document/11245717
[11] Farquhar, S., Kossen, J., Kuhn, L., et al. (2024). Detecting hallucinations in large language models using semantic
entropy. Nature, 630, 625–630. https://doi.org/10.1038/s41586-024-07421-0
[12] Menshawy, A., Nawaz, Z., & Fahmy, M. (2024). Navigating challenges and technical debt in large language models
deployment. EuroMLSys ’24, 192–199. https://doi.org/10.1145/3642970.3655840
[13] Diaz-De-Arcaya, J., & López-De-Armentia, J. (2024). Large language model operations (LLMOps): Definition,
challenges, and lifecycle management. IEEE Conference Proceedings.
[14] Pahune, S., & Akhtar, Z. (2025). Transitioning from MLOps to LLMOps: Navigating the unique challenges of large
language models. Information, 16(2), 87. https://www.mdpi.com/2078-2489/16/2/87
[15] Diaz-De-Arcaya, J., & López-De-Armentia, J. (2024). Large language model operations (LLMOps): Definition,
challenges, and lifecycle management. IEEE Conference Proceedings. https://ieeexplore.ieee.org/document/10612341
[16] Özer, F. (2025). Systematic technical survey on LLMOps: Lifecycle, tools, challenges, and emerging practices.
University of Eastern Finland. https://erepo.uef.fi/bitstreams/4212c75f-1822-4648-aa2c-7c55c55d86c2/download
[17] Joshi, S. (2025). LLMOps, AgentOps, and MLOps for Generative AI: A Comprehensive Review. PhilPapers.
https://philpapers.org/archive/JOSLAA-3.pdf
[18] Menshawy, A., Nawaz, Z., & Fahmy, M. (2024). Navigating challenges and technical debt in large language models
deployment. Proceedings of the 4th Workshop on Machine Learning and Systems (EuroMLSys ’24), 192–199.
https://doi.org/10.1145/3642970.3655840
[19] Aryan, A. (2025). LLMOps: Managing Large Language Models in Production. Springer / Google Books.
https://books.google.com/books?id=PrZxEQAAQBAJ
[20] Nicora, G. (2025). MLOps and LLMOps: Development of an LLM-based application with focus on scalable cloud
deployment. Politecnico di Torino (Master’s Thesis). https://webthesis.biblio.polito.it/36459/
[21] Farquhar, S., Kossen, J., Kuhn, L., et al. (2024). Detecting hallucinations in large language models using semantic
entropy. Nature, 630, 625–630. https://doi.org/10.1038/s41586-024-07421-0
[22] Shan, R., & Shan, T. (2024). Enterprise LLMOps: Advancing large language models operations practice. IEEE
Cloud Summit. https://ieeexplore.ieee.org/document/10630923
current
state
[23] Radenkovic, B., & Prokhorov, S. (2025). Application of large language models in software development: Review of
the
and
development
perspectives.
IEEE
Conference
Proceedings.
https://ieeexplore.ieee.org/document/11245717
[24] Fitsilis, P., Damasiotis, V., &Kyriatzis, V. (2024). DOLLmC: DevOps for large language model customization.
arXiv preprint arXiv:2405.11581. https://arxiv.org/abs/2405.11581
[25] Tantithamthavorn, C. K., Palomba, F., Khomh, F., & Chua, J. J. (2024). MLOps, LLMOps, FMOps, and beyond.
IEEE Software. https://www.computer.org/csdl/magazine/so/2024/01
[26] Dong, L., Lu, Q., & Zhu, L. (2024). AgentOps: Enabling Observability of LLM Agents. arXiv:2411.05285.
https://arxiv.org/abs/2411.05285
[27] Xu, X., Weytjens, H., Zhang, D., Lu, Q., & Weber, I. (2025). RAGOps: Operating and Managing Retrieval
Augmented Generation Pipelines. arXiv:2506.03401. https://arxiv.org/abs/2506.03401
[28]
Aryan, A. (2025). LLMOps: Managing Large Language Models in Production. Springer.
https://books.google.com/books?id=PrZxEQAAQBAJ
[29] Daga, V., Daga, P., & Dhabhai, A. (2025). Energy and cost optimization strategies for large language models in
LLMOps. International Journal of Recent Research and Review. https://ijrrr.com/specialissues2-2025/ijrrr-Special
Issue-2-2025-paper42.pdf
[30] Nicora, G. (2025). MLOps and LLMOps: Development of an LLM-based application with focus on scalable cloud
deployment. Politecnico di Torino. https://webthesis.biblio.polito.it/36459/
[31] Polyakovska, N. (2025). Understanding and building operational excellence for large language models. Computer
Integrated Technologies. https://cit.lntu.edu.ua/index.php/cit/article/view/691
for
[32] Devarapalli, S., Vathsavai, V. G., & Katkam, V. (2025). Cloud-native LLMOps meets DataOps: A unified
framework
high-volume
data
analytical
systems.
IEEE
Conference
Proceedings.
https://ieeexplore.ieee.org/document/11158069
[33] Gadiraju, P., Kosna, S. R., & Shah, K. (2025). DataOps meets LLMOps: Automating cloud-based AI workflows
from
ingestion
to
prompt
optimization.
IEEE
Conference
Proceedings.
https://ieeexplore.ieee.org/document/11135202
[34] Doan, R. (2024). Essential guide to LLMOps. Packt Publishing.
[35] Abbassi, A. A. (2025). Towards Reliable and Trustworthy Pipelines for MLOps and LLMOps. PhD Dissertation,
Polytechnique Montréal.
[36] Borovits, N., & Tamburri, D. A. (2025). On the Maturity of LLMOps Services Computing: An Industrial Study.
Service-Oriented Computing, Springer.
[37] Tantithamthavorn, C. K., Palomba, F., Khomh, F., & Chua, J. J. (2024). MLOps, LLMOps, FMOps, and Beyond.
IEEE Software.
[38] Joshi, S. (2025). LLMOps, AgentOps, and MLOps for Generative AI: A Comprehensive Review. PhilPapers.
[39] Özer, F. (2025). Systematic Technical Survey on LLMOps: Lifecycle, Tools, Challenges, and Emerging Practices.
University of Eastern Finland.
[40] Lekota, N. F. (2024). Governance considerations of adversarial attacks on AI systems. Proceedings of the
International Conference on AI Security.
[41] Brundage, M., et al. (2023). Toward trustworthy AI development: Mechanisms for security and governance.
arXiv:2302.XXXX.
