LLM and Infrastructure as a Code use case

TL;DR

This paper explores using large language models to automatically generate Ansible YAML configurations from human descriptions, aiming to enhance DevOps automation in cloud infrastructure management.

Contribution

It introduces a novel approach for translating natural language descriptions into Ansible code using generative LLMs, addressing automation challenges in DevOps workflows.

Findings

Identified promising directions for LLM-based code generation

Outlined potential industrial applications of the approach

Compared LLM methods with existing tools like Terraform

Abstract

Cloud computing and the evolution of management methodologies such as Lean Management or Agile entail a profound transformation in both system construction and maintenance approaches. These practices are encompassed within the term "DevOps." This descriptive approach to an information system or application, alongside the configuration of its constituent components, has necessitated the development of descriptive languages paired with specialized engines for automating systems administration tasks. Among these, the tandem of Ansible (engine) and YAML (descriptive language) stands out as the two most prevalent tools in the market, facing notable competition mainly from Terraform. The current document presents an inquiry into a solution for generating and managing Ansible YAML roles and playbooks, utilizing Generative LLMs (Language Models) to translate human descriptions into code. Our efforts are focused on identifying plausible directions and outlining the potential industrial applications. Note: For the purpose of this experiment, we have opted against the use of Ansible Lightspeed. This is due to its reliance on an IBM Watson model, for which we have not found any publicly available references. Comprehensive information regarding this remarkable technology can be found [1] directly on our partner's website, RedHat.