Prompt-Driven and Kubernetes Error Report-Aware Container Orchestration

Abstract

Background: Container orchestration systems like Kubernetes rely heavily on declarative manifest files, which serve as orchestration blueprints. However, managing these manifest files is often complex and requires substantial DevOps expertise. Methodology: This study investigates the use of Large Language Models (LLMs) to automate the creation of Kubernetes manifest files from natural language specifications, utilizing prompt engineering techniques within an innovative error- and warning-report–aware refinement process. We assess the capabilities of these LLMs using Zero-Shot, Few-Shot, Prompt-Chaining, and Self-Refine methods to address DevOps needs and support fully automated deployment pipelines. Results: Our findings show that LLMs can generate Kubernetes manifests with varying levels of manual intervention. Notably, GPT-4 and GPT-3.5 demonstrate strong potential for deployment automation. Interestingly, smaller models sometimes outperform larger ones, challenging the assumption that larger models always yield better results. Conclusions: This research highlights the crucial impact of prompt engineering on LLM performance for Kubernetes tasks and recommends further exploration of prompt techniques and model comparisons, outlining a promising path for integrating LLMs into automated deployment workflows.