Everyone seems to be talking about generative AI. Tools like ChatGPT and GitHub’s Copilot promise to revolutionise the way developers and operations engineers work. As someone who spends a lot of time writing infrastructure code and troubleshooting systems, I’ve been curious about how these tools actually fit into a DevOps workflow.

Coding assistants for Infrastructure as Code

I was initially sceptical of AI code generation. How could a language model understand the nuances of my Terraform modules or Kubernetes manifests? Surprisingly, Copilot has been most helpful for boilerplate code: writing resource blocks, suggesting module inputs, or drafting basic YAML for GitHub Actions. It won’t design your entire infrastructure, but it does save you from typing repetitive blocks. For example, when creating IAM policies, Copilot can scaffold the JSON and remind you of required keys.

However, you must review and validate everything it produces. AI often hallucinates resource names or makes unsafe assumptions. Use it as a starting point, not as an authority. Running terraform validate and scanning changes with tfsec or checkov remain essential.

Natural language interfaces

ChatGPT shines when you need to explain concepts or debug unfamiliar error messages. I’ve used it to:

  • Translate AWS errors into plain English and suggest possible causes.
  • Draft Terraform documentation or README snippets.
  • Brainstorm naming conventions for infrastructure resources.

ChatGPT’s conversational nature makes it easy to ask follow‑up questions, but again, double‑check its answers against official documentation. The model may be out of date or simply incorrect.

AI‑powered search and knowledge management

One of the hidden productivity drains in DevOps is context switching: switching between Slack threads, Confluence pages and GitHub issues to find the information you need. Several new tools aim to index your company’s knowledge bases and provide semantic search. Instead of memorising where the incident runbook lives, you can ask “How do I rotate the database credentials?” and get a relevant document. This aligns with the idea of AI as a force multiplier rather than a replacement.

Guardrails and ethics

Using AI responsibly means considering privacy and security. Don’t paste secrets into ChatGPT prompts. Be cautious about uploading proprietary code to third‑party tools. Some organisations are experimenting with self‑hosted models to avoid data leakage. Additionally, remember that AI may embed biases or amplify bad practices; treat its output the same way you’d treat a junior engineer’s suggestions: helpful but requiring review.

Conclusion

AI is not going to replace DevOps engineers, but it can make us more efficient. By handling boilerplate code, summarising documentation and providing intelligent search, AI frees us to focus on architecture, optimisation and collaboration. As with any tool, the key is to understand its limitations and integrate it thoughtfully into your workflow.