Hands-on educational repo for DevOps engineers learning to integrate AI and LLMs into their workflows.
This repository was created as part of an internal workshop to bring clarity and structure to the growing world of AI tools in the context of DevOps engineering.
It is designed for beginners who want to:
- Understand how AI and LLMs can be practically applied to DevOps workflows
- Learn foundational concepts through clear, working demos
- Explore real-world use cases (incident analysis, CI feedback, chatbot integrations, etc.)
- Get inspired to build their own AI-powered operational tools
This is not a product, library, or production-ready toolkit. It is a learning-focused resource built for internal knowledge-sharing.
- Introduce common DevOps processes where AI/LLMs can provide value
- Present a practical, example-based learning environment
- Demonstrate best practices in prompt design, inference orchestration, and automation flow
- Help teams identify areas in their infrastructure where AI can reduce toil and accelerate insight
All code in this repository is intended for demo purposes only. While the examples are functional, they are:
- Not security hardened
- Not optimized for performance
- Not tested at scale
They are meant to provide a strong starting point, not a plug-and-play solution.
Use these examples as inspiration and modify them before applying to production environments.
ai-driven-devops-workshop/
├── 01-llm-basics/
├── 02-log-summarization/
├── 03-alert-analysis/
├── 04-prompt-engineering/
├── 05-rag-with-dev-docs/
├── 06-ci-integration/
├── 07-cli-agent-demo/
├── 08-tool-execution/
├── 09-langchain-basic/
├── Makefile
├── requirements.txt
└── README.md- DevOps engineers with minimal or no prior experience in AI
- Platform engineers curious about LLM use cases
- Anyone interested in automating infrastructure reasoning and decision making
- Python (OpenAI API, LangChain or similar)
- Bash (for CI hooks and local integration)
- GitHub Actions / Jenkins (as examples)
- Optional: Docker / n8n / FastAPI for orchestration
This repository is maintained as part of an internal learning initiative. Contributions, improvements, and suggestions are welcome as long as they align with the spirit of education and clarity.
Let’s bring AI into DevOps – the right way.