Abstract

The article describes the methodological foundations of AI observability for large language models integrated into enterprise environments. The relevance is determined by the adoption of LLMs in business alongside the emergence of specific risks: hallucinations, data leaks, and uncontrolled cost escalation that are not covered by traditional monitoring tools. The scientific novelty lies in proposing a multi-level framework that systematizes observability metrics across four dimensions: quality and semantics, cost and performance, security and privacy, responsibility and ethics. The study identifies the limitations of classical MLOps approaches as applied to LLMs and analyzes contemporary methods for detecting anomalous model behavior. Special emphasis is placed on coupling automated metrics with human feedback mechanisms. The purpose of the study is to construct a holistic methodology for designing LLM observability systems. To achieve this goal, methods of systems and comparative analysis, as well as conceptual architectural modeling, are employed. In conclusion, the practical significance of the framework is demonstrated for minimizing risks and increasing the return on investment from LLM applications. The findings presented in this work will be of interest to project managers in Data Science, MLOps engineers, and AI systems architects.

Keywords

  • AI Observability
  • large language models
  • LLM
  • LLMOps
  • AI monitoring
  • enterprise AI
  • hallucination detection
  • responsible AI
  • Conversational AI
  • MLOps.

References

  1. 1. Chang, Y., Wang, X., Wang, J., Wu, Y., Yang, L., Zhu, K., & Xie, X. (2024). A survey on evaluation of large language models. ACM transactions on intelligent systems and technology, 15(3), 1-45. https://doi.org/10.1145/3641289
  2. 2. Xu, F. F., Alon, U., Neubig, G., & Hellendoorn, V. J. (2022, June). A systematic evaluation of large language models of code. In Proceedings of the 6th ACM SIGPLAN international symposium on machine programming (pp. 1-10). https://doi.org/10.1145/3520312.3534862
  3. 3. Shethiya, A. S. (2023). Rise of LLM-Driven Systems: Architecting Adaptive Software with Generative AI. Spectrum of Research, 3(2), 1-8.
  4. 4. Su, W., Tang, Y., Ai, Q., Wang, C., Wu, Z., & Liu, Y. (2024, December). Mitigating entity-level hallucination in large language models. In Proceedings of the 2024 Annual International ACM SIGIR Conference on Research and Development in Information Retrieval in the Asia Pacific Region (pp. 23-31). https://doi.org/10.1145/3673791.3698403 .
  5. 5. Yao, Y., Duan, J., Xu, K., Cai, Y., Sun, Z., & Zhang, Y. (2024). A survey on large language model (llm) security and privacy: The good, the bad, and the ugly. High-Confidence Computing, 4(2), 100211.https://doi.org/10.1016/j.hcc.2024.100211
  6. 6. Makridakis, S., Petropoulos, F., & Kang, Y. (2023). Large Language Models: Their Success and Impact. Forecasting, 5(3), 536-549. https://doi.org/10.3390/forecast5030030
  7. 7. Natarajan, S., Mathur, S., Sidheekh, S., Stammer, W., & Kersting, K. (2025, April). Human-in-the-loop or AI-in-the-loop? Automate or Collaborate?. In Proceedings of the AAAI Conference on Artificial Intelligence, 39 (27), 28594-28600. https://doi.org/10.1609/aaai.v39i27.35083
  8. 8. Palit, S., & Woods, D. (2025). Evaluating the efficacy of LLM Safety Solutions: The Palit Benchmark Dataset. arXiv preprint arXiv:2505.13028. https://doi.org/10.48550/arXiv.2505.13028 .
  9. 9. Tavasoli, A., Sharbaf, M., & Madani, S. M. (2025). Responsible innovation: A strategic framework for financial LLM integration. arXiv preprint arXiv:2504.02165.
  10. https://doi.org/10.48550/arXiv.2504.02165 .
  11. 10. Ganesan, P. (2024). LLM-Powered Observability Enhancing Monitoring and Diagnostics. J Artif Intell Mach Learn & Data Sci, 2(2), 1329-1336. https://doi.org/10.51219/JAIMLD/premkumar-ganesan/304