# LADs: Leveraging LLMs for AI-Driven DevOps

arXiv: https://arxiv.org/abs/2502.20825 · HTML: https://mlsanity.com/paper/2502.20825/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 14 of its 37 references that are in the map.
- First posted on arXiv 28 Feb 2025.

## Builds on (most cited first, 14 of 14)

- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks](https://mlsanity.com/paper/2005.11401/) (2020)
- [Finetuned Language Models Are Zero-Shot Learners](https://mlsanity.com/paper/2109.01652/) (2021)
- [Billion-scale similarity search with GPUs](https://mlsanity.com/paper/1702.08734/) (2017)
- [Making Pre-trained Language Models Better Few-shot Learners](https://mlsanity.com/paper/2012.15723/) (2020)
- [The Power of Noise: Redefining Retrieval for RAG Systems](https://mlsanity.com/paper/2401.14887/) (2024)
- [An Empirical Study of the Non-determinism of ChatGPT in Code Generation](https://mlsanity.com/paper/2308.02828/) (2023)
- [Improving LLM Reasoning through Scaling Inference Computation with Collaborative Verification](https://mlsanity.com/paper/2410.05318/) (2024)
- [GPTuner: A Manual-Reading Database Tuning System via GPT-Guided Bayesian Optimization](https://mlsanity.com/paper/2311.03157/) (2023)
- [QualityFlow: An Agentic Workflow for Program Synthesis Controlled by LLM Quality Checks](https://mlsanity.com/paper/2501.17167/) (2025)
- [GenKubeSec: LLM-Based Kubernetes Misconfiguration Detection, Localization, Reasoning, and Remediation](https://mlsanity.com/paper/2405.19954/) (2024)
- [PreConfig: A Pretrained Model for Automating Network Configuration](https://mlsanity.com/paper/2403.09369/) (2024)
- [Service Provider DevOps](https://mlsanity.com/paper/1702.06387/) (2017)
- [A Multi-Tenant Framework for Cloud Container Services](https://mlsanity.com/paper/2103.13333/) (2021)

## Built on by (most cited first, 4 of 4)

- [SetupBench: Assessing Software Engineering Agents' Ability to Bootstrap Development Environments](https://mlsanity.com/paper/2507.09063/) (2025)
- [KubeGuard: LLM-Assisted Kubernetes Hardening via Configuration Files and Runtime Logs Analysis](https://mlsanity.com/paper/2509.04191/) (2025)
- [Large Language Models for IT Automation Tasks: Are We There Yet?](https://mlsanity.com/paper/2505.20505/) (2025)
- [CoE-Ops: Collaboration of LLM-based Experts for AIOps Question-Answering](https://mlsanity.com/paper/2507.22937/) (2025)

## Abstract (arXiv)

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.