# AI For DevOps

A 21-day, day-by-day curriculum teaching DevOps engineers to apply large language models and generative AI to their work, covering LLM fundamentals, prompt engineering, RAG, CI/CD, Kubernetes and building production-style AI-assisted projects.

AI For DevOps is a 21-day structured course aimed at DevOps engineers who want to add AI to their daily workflow. The first week covers LLM and generative AI foundations: how tokens, context windows, attention and model parameters work, prompt engineering techniques such as few-shot prompting and prompt chaining, working with LLM APIs, and building a provider-agnostic integration layer that abstracts differences between model vendors.

Later material moves into applied topics such as retrieval-augmented generation, DevOps automation with AI, and AI-assisted CI/CD and Kubernetes workflows, working toward three hands-on builds: an LLM assistant, a RAG engine, and a self-healing autonomous agent.

The course is distributed as a paid ebook with an accompanying visual system-flow diagram for each concept, interview-style question sets, and example Docker and LangGraph setups; the repository itself hosts the outline and a link to the full material.

Website: https://github.com/saghosh8/AI-For-DevOps