Enhancing Ansible Playbooks with Large Language Models:Revolutionizing Automation

Abstract

This research explores the application of large language models (LLMs) in automating the creation of Ansible playbooks. We examine how AI-powered tools like IBM's watsonx Code Assistant for Red Hat Ansible Lightspeed leverage natural language processing to generate infrastructure-as-code from plain English prompts. The study investigates the effectiveness of these systems in reducing development time, improving code quality, and lowering the barrier to entry for IT automation. Our analysis covers the underlying LLM architecture, prompt engineering techniques, and integration with existing DevOps workflows. Experimental results demonstrate significant productivity gains, with AI-generated playbooks requiring 40% less time to develop compared to manual authoring. However, we also identify limitations around complex logic and enterprise-specific requirements. The findings suggest AI assistants show promise in accelerating routine automation tasks, but human oversight remains crucial for production-grade playbooks.