Automating cloud configuration and deployment remains a critical challenge due to evolving infrastructures, heterogeneous hardware, and fluctuating workloads. Existing solutions lack adaptability and require extensive manual tuning, leading to inefficiencies and misconfigurations. We introduce LADs, the first LLM-driven framework designed to tackle these challenges by ensuring robustness, adaptability, and efficiency in automated cloud management. Instead of merely applying existing techniques, LADs provides a principled approach to configuration optimization through in-depth analysis of what optimization works under which conditions. By leveraging Retrieval-Augmented Generation, Few-Shot Learning, Chain-of-Thought, and Feedback-Based Prompt Chaining, LADs generates accurate configurations and learns from deployment failures to iteratively refine system settings. Our findings reveal key insights into the trade-offs between performance, cost, and scalability, helping practitioners determine the right strategies for different deployment scenarios. For instance, we demonstrate how prompt chaining-based adaptive feedback loops enhance fault tolerance in multi-tenant environments and how structured log analysis with example shots improves configuration accuracy. Through extensive evaluations, LADs reduces manual effort, optimizes resource utilization, and improves system reliability. By open-sourcing LADs, we aim to drive further innovation in AI-powered DevOps automation.

翻译：自动化云配置与部署因基础设施不断演进、硬件异构化及工作负载动态波动而持续面临严峻挑战。现有解决方案缺乏适应性，需大量人工调优，导致效率低下与配置错误。本文提出LADs——首个基于大语言模型的框架，通过确保自动化云管理的鲁棒性、适应性与效率来应对这些挑战。LADs并非简单套用现有技术，而是通过深入分析"何种优化策略在何种条件下有效"，为配置优化提供原理性方法。通过融合检索增强生成、少样本学习、思维链及基于反馈的提示链技术，LADs能生成精准配置，并从部署故障中学习以迭代优化系统设置。我们的研究揭示了性能、成本与可扩展性之间的关键权衡关系，帮助从业者为不同部署场景制定适宜策略。例如，我们展示了基于提示链的自适应反馈循环如何增强多租户环境下的容错能力，以及结合示例样本的结构化日志分析如何提升配置精度。大量评估表明，LADs能显著减少人工干预、优化资源利用率并提升系统可靠性。通过开源LADs框架，我们旨在推动人工智能驱动的DevOps自动化领域持续创新。