Automating software development processes through the orchestration of GitHub Action workflows has revolutionized the efficiency and agility of software delivery pipelines. This paper presents a detailed investigation into the use of Large Language Models (LLMs) specifically, GPT 3.5 and GPT 4 to generate and evaluate GitHub Action workflows for DevOps tasks. Our methodology involves data collection from public GitHub repositories, prompt engineering for LLM utilization, and evaluation metrics encompassing exact match scores, BLEU scores, and a novel DevOps Aware score. The research scrutinizes the proficiency of GPT 3.5 and GPT 4 in generating GitHub workflows, while assessing the influence of various prompt elements in constructing the most efficient pipeline. Results indicate substantial advancements in GPT 4, particularly in DevOps awareness and syntax correctness. The research introduces a GitHub App built on Probot, empowering users to automate workflow generation within GitHub ecosystem. This study contributes insights into the evolving landscape of AI-driven automation in DevOps practices.

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