A Retrieval-Augmented Generation Framework for AI-Assisted Cloud Operations
DOI:
https://doi.org/10.63282/3117-5481/AIJCST-V2I6P104Keywords:
Retrieval-Augmented Generation, Large Language Models, Cloud Operations, Aiops, Cloud Management, Knowledge Retrieval, Incident Management, Intelligent AutomationAbstract
As more and more operational data is now provided as a collection of heterogeneous data sources, such as metrics from monitoring systems, system logs, distributed traces, configuration information, incident reports, and operational documentation, traditional approaches to automation are difficult to apply in a timely manner and with effectiveness. While Large Language Models (LLMs) are powerful for understanding operational data and creating natural language recommendations, they may struggle with relevant information that is not recent, missing context for cloud-specific operations, and hallucinating answers. In this paper, the authors suggest a new extended Retrieval-Augmented Generation (RAG) model for AI-assisted cloud operations, which combines real-time cloud operational data with a continually growing knowledge base. The framework includes data collection, data pre-processing, semantic indexing, knowledge retrieval according to the query, the construction of the context prompt, and LLM-based reasoning to deliver operational insights grounded in data for incident diagnosis, root cause analysis, troubleshooting, and remediation planning.The framework is tested with a set of representative cloud operational workloads and is contrasted with a traditional rule-based monitoring, machine-learning-based monitoring and standalone LLM-based operations. It takes into account the relevance of retrieval, the correctness of diagnosis, the quality of the response, the effectiveness of grounding, the time for the first detection, the speed of response, the use of resources and scalability in the case of a growing operational workload. Experimental results show that operational knowledge retrieval enhances the contextual accuracy and reliability of LLM-generated responses and helps to reduce unsupported recommendations and improve operational decision making. The architecture is scalable, enabling seamless integration of the RAG and LLM technologies into cloud operations, and lays the groundwork for more intelligent, context-aware, and human-supervised automation of cloud incident management.
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