May 12 • AI & Automation
6 AI Prompts That Save Cloud Engineers Hours Every Week
Most cloud engineers use AI like a search engine. Paste an error, get a generic answer, go back to Googling.
That's the wrong approach.
The right way: treat AI as a junior engineer who reads everything instantly but needs precise instructions to produce useful output. Vague prompts produce vague output. Specific prompts produce tools.
Here are 6 prompts I use every week. Copy them, adjust for your cloud provider, use them on real work today.
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- TERRAFORM PLAN REVIEW
Paste your terraform plan output and use this prompt:
"You are a cloud security and cost engineer. Review this Terraform plan and identify:
- Resources that will be publicly exposed (public IPs, open security groups, public S3 buckets)
- - IAM permissions broader than the stated purpose requires
- - Resources likely to cause unexpectedly high costs
- - Missing tags that would block cost attribution
Format: Risk | Severity (High/Med/Low) | Line Reference | Recommended Fix
[paste terraform plan here]"
You get a structured risk table in 10 seconds. Doing this manually takes 30+ minutes.
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- LOG ANALYSIS (CloudWatch / Azure Monitor / GCP Logs Explorer)
When you're staring at 400 lines of log output:
"You are a cloud operations engineer. Analyze these logs and:
- Identify the root cause of any errors or warnings
- - Show the sequence of events that led to the issue
- - Suggest 3 specific remediation steps with CLI commands where possible
[paste logs here]"
Works identically with CloudWatch Logs Insights, Azure Monitor, or GCP Logs Explorer output. Just paste and run.
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- IAM POLICY AUDIT
Paste any IAM policy JSON and ask:
"You are an AWS IAM security specialist. Review this policy and identify:
- Actions that grant broader access than the stated purpose requires
- - Any privilege escalation paths
- - Resources scoped too broadly (e.g., * where a specific ARN should be used)
- - Specific changes needed to enforce least-privilege
[paste IAM policy JSON here]"
Same prompt works for Azure role definitions and GCP IAM bindings. Just change the specialist label at the top.
---
- INCIDENT SUMMARY FOR POSTMORTEMS
During or after an incident, paste your Slack thread or notes and use:
"You are an SRE writing a blameless postmortem. Using these incident notes, produce:
- A 3-sentence executive summary in non-technical language
- - A timeline of key events with timestamps
- - A specific, technical root cause statement
- - 5 action items with suggested owners and target deadlines
[paste incident notes / Slack thread / logs here]"
Raw Slack chaos to structured postmortem in under 2 minutes. Send the exec summary to leadership, the full doc to the team.
---
- SCRIPT GENERATION FROM PLAIN LANGUAGE
Stop writing boilerplate from scratch. Example:
"Write a Bash script using the AWS CLI that:
- Lists all EC2 instances in us-east-1 stopped for more than 7 days
- - Outputs: instance ID, name tag, stop date, estimated monthly cost (use t3.medium pricing as baseline)
- - Formats output as CSV with headers
- - Includes error handling and a --dry-run flag"
Test the output. Adjust the specifics. It gets you 80% of the way there in under a minute.
The same pattern works for Azure CLI + PowerShell and gcloud scripts. Just swap the tool name and describe the exact output format you need.
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- CLIENT COST REPORT FROM RAW DATA
You have raw Cost Explorer data. You need a paragraph a non-technical client can read:
"You are a cloud consultant writing a monthly cost report for a non-technical client. Convert this cost data into:
- A 2-paragraph executive summary explaining what changed and why
- - 3 cost reduction recommendations in plain business language (no cloud acronyms)
- - An estimated monthly savings range for each recommendation
Write professionally but avoid AWS/Azure/GCP jargon throughout.
[paste cost data here]"
This turns a data dump into a client-ready section in 60 seconds. Works whether your data came from AWS Cost Explorer, Azure Cost Management, or GCP Billing export.
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ONE RULE BEFORE YOU USE ANY OF THESE:
Verify the output before acting on it.
AI gets ARNs wrong, confuses regions, and occasionally recommends something that will cause problems in prod. Read what it gives you. Test in non-prod first. The engineering judgment stays with you.
These prompts save time on research and first drafts. The rest is still your job.
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Which of these are you going to use first? Drop it in the comments below — or share a prompt you've been using that I didn't list. The best ones will go into next week's resource pack.
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6 AI Prompts That Save Cloud Engineers Hours Every Week
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