AI Prompts for DevOps Engineers (2026)

Treat every prompt as a fill-in-the-blank form. Replace each [bracketed] placeholder with your real values — service names, cloud provider, error text, log snippets — and delete any placeholder that does not apply. The more specific the inputs, the less the model invents.

Always paste the model a **source of truth**: the actual Terraform plan, the real alert payload, the genuine stack trace. Models reason far better over your concrete artifacts than over a vague description. Run output through your normal review gate — a prompt drafts the runbook; you still own the merge.

**Never paste secrets, production credentials, customer PII, or private keys into a chatbot.** Redact tokens, account IDs, and IPs before pasting. For the broader risk model, see the prompt injection defense checklist — your CI pipeline and any agent that reads logs are attack surfaces too.

Runbooks rot fast and nobody wants to write them. These two turn a rough description into a structured, on-call-ready document.

**1. Generate an on-call runbook** — "You are a senior SRE writing an on-call runbook. Service: [service name]. It does: [one-sentence purpose]. Runs on: [platform e.g. EKS/ECS/bare VM]. Common failure modes I know about: [list]. Produce a runbook with these sections: Overview, Architecture summary, Key dashboards and metrics to check first, Symptom -> likely cause -> remediation table, Escalation path, and Rollback steps. Use imperative commands. Mark any step that is destructive with a WARNING. Where a command depends on my environment, leave a [BRACKET] for me to fill in rather than guessing."

**2. Turn an incident into a runbook entry** — "Here is a resolved incident: [paste timeline + root cause + fix]. Extract a reusable runbook entry: the exact symptom an on-call engineer would observe, the detection signal (alert name or query), the fastest safe remediation, and a 'do not do this' note capturing what made it worse during the incident. Output as a single markdown section under a clear symptom heading."

**3. Document a deployment process** — "Document the deployment process for [service]. Inputs I will give you: CI tool is [tool], artifact is [container/binary/package], target is [environment]. Write a numbered, copy-pasteable deploy checklist that includes pre-deploy verification, the deploy command, health-check verification, and a rollback command. Flag any step where a human must confirm before proceeding."

Use these to get a fast second pair of eyes on Terraform, Helm, or Kubernetes manifests before they hit review. Paste the actual file content.

**4. Review a Terraform plan for risk** — "You are a cautious infrastructure reviewer. Here is a `terraform plan` output: [paste plan]. List every change that is destructive or potentially disruptive (replacements, deletions, changes to networking, IAM, or data stores) as a ranked risk table: change, blast radius, what to verify before applying. Do not approve or reassure — only surface risk. If something would cause downtime or data loss, say so plainly at the top."

**5. Review a Kubernetes manifest** — "Review this Kubernetes manifest for production-readiness: [paste YAML]. Check for: missing resource requests/limits, missing liveness/readiness probes, running as root, no securityContext, latest tags, and missing pod disruption budgets or anti-affinity. Output a checklist of findings with severity (high/medium/low) and the exact YAML snippet to add or change for each. Do not rewrite the whole file — give targeted diffs."

**6. Explain unfamiliar IaC** — "Explain what this infrastructure code does as if onboarding a new engineer: [paste Terraform/Helm/Pulumi]. Cover what resources it creates, how they connect, what it costs roughly in category terms (compute/storage/network), and any non-obvious dependency or footgun. End with three questions I should ask the original author before changing it."

During an incident, writing is the bottleneck. These produce calm, accurate status updates fast — you supply the facts, the model supplies the structure.

**7. Draft an incident status update** — "You are an incident commander writing a status update for [audience: internal Slack / public status page / executives]. Facts: severity is [SEV level], impact is [what users experience], started at [time], current status is [investigating/identified/monitoring/resolved], next update in [time]. Write a concise update in plain language. Do not speculate about root cause beyond what I gave you. Do not commit to a fix time unless I provided one. Keep it under [N] words."

**8. Translate technical detail for executives** — "Here is the technical situation: [paste]. Rewrite it as a 4-sentence executive update: what is broken in business terms, who is affected, what we are doing, and when the next update comes. No jargon, no blame, no internal service names the reader will not recognize."

Close the loop after the fire is out, and unblock pipelines faster while one is burning.

**9. Draft a blameless postmortem** — "You are facilitating a blameless postmortem. Inputs: timeline [paste], root cause [paste or 'unknown — help me reason about it'], impact [paste]. Produce a postmortem with: summary, impact, timeline (as a table), contributing factors (systemic, not individual), what went well, and a list of corrective action items each with a suggested owner-role and priority. Keep language blameless — describe what the system allowed, not who erred. Mark any action item that is a quick win."

**10. Debug a failing CI/CD pipeline** — "My CI pipeline is failing. Tool: [tool]. Here is the failing job log: [paste log, secrets redacted]. Here is the relevant config: [paste]. Identify the most likely cause, explain the evidence in the log that points to it, and give the specific fix. If there are multiple plausible causes, rank them and tell me the single fastest check to disambiguate. Do not suggest disabling the failing check as a fix."

**Do not paste secrets or production credentials.** API keys, kubeconfigs with live tokens, database connection strings, and account IDs should be redacted before they touch a chatbot. Assume anything pasted may be logged.

**Do not let the model invent commands for your environment.** If a prompt produces `kubectl` or `aws` commands referencing resources it cannot know, it is guessing. Use the bracket-placeholder pattern above so the model marks unknowns instead of fabricating them, and verify every destructive command before running it.

**Do not trust risk assessments blindly, and do not skip review.** An AI Terraform review is a fast first pass, not an approval. The model has no view of your real blast radius, your data, or your compliance constraints. Treat output as a draft that a human owns. For the techniques that make these prompts reliable, see how to write a system prompt and chain-of-thought prompting.