Most DevOps teams are starting to use AI for coding, Terraform, CI/CD, scripts, and PR reviews.

But the real problem is not “can AI generate code?”

The real problem is:

- who reviews the output?

- what guardrails does the repo give the AI?

- how do you stop AI from creating insecure Terraform?

- how do you keep CI/CD changes reviewable?

- how do you prevent copy-paste automation from becoming production risk?

I built a practical AI Coding / DevOps workflow around this idea:

AI should speed up engineering work, but it should not bypass engineering judgment.

The workflow I recommend:

1. Give AI clear repo instructions

2. Use a PR review checklist

3. Review Terraform/IaC for security and cost risk

4. Add CI/CD guardrails before merge

5. Keep human approval for anything production-facing

If you are using ChatGPT, Cursor, Copilot, Claude, or any AI coding tool for cloud/devops work, this is the part most teams skip.

I also made a free starter kit for IT/cloud people who want a safer AI workflow:

And for developers / DevOps engineers, I packaged the full AI Coding Bundle here:

Question for the group:

Where are you already using AI in your workflow?

- Terraform / IaC

- CI/CD

- scripts

- PR reviews

- incident notes

- documentation

- something else?

0 comments

skool.com/cloud-cost-optimization-3746

Automate your cloud work with AI. GCP, Azure, VMware. Save hours every week with real workflows.

Bring people together around your passion and get paid.