← AI for CloudField Guide · Prompt Engineering for CloudBecome AI-ready as a cloud engineer in one day.
One reusable prompt frame at 9 AM, then eight copy-paste recipes until your AI writes cloud work you'd actually ship — architecture, Terraform, Kubernetes manifests, CI/CD pipelines, debugging, scripts, incident summaries, and runbooks.
// 09:00 · Warm-upThe R·C·T·F Frame
Every recipe below builds on these four moves. Learn it once — reuse it forever.
R
Role
Tell the model who to be. A persona sets vocabulary, rigor, and defaults.
"Act as a senior cloud architect with 10 years in AWS and Terraform."
C
Context
Feed the facts: cloud, stack, constraints, and the source material to work from.
"Cloud: AWS. Workload: EKS + RDS. Constraint: <2s p99, PCI scope. Here's the current setup: …"
T
Task
One verb, one deliverable. Be explicit about scope and depth.
"Write a Terraform module for a VPC with public/private subnets and a NAT gateway."
F
Format
Pin the shape of the answer so it drops straight into your repo or tools.
"Output complete .tf files + a variables table; no hardcoded secrets."
+ Power-ups
Examples — paste 1–2 of your real configs so it matches your house style. Constraints — "least-privilege IAM, encrypt at rest, no public buckets". Iterate — "now add an HPA and probes" beats rewriting from scratch.
// The Day09:00 → 17:00 at a glance
Morning designs and provisions the infra. Afternoon debugs, automates, and documents it.
09Architecture
10Terraform
11Kubernetes
12CI/CD
13Lunch
14Debug
15Scripting
16Incidents
17Runbooks
Morning · Design & Provision09:00 – 12:00 · plan before you apply
09:00ARCHITECTURE01
Architecture & Design
Produce a cost- and risk-aware design for the workload — services, topology, trade-offs.
architecture.md
Act as a cloud architect. We're building [workload/feature] for [users/scale].
Cloud: [AWS / Azure / GCP]. Constraints: [budget, latency, compliance].
Propose a reference architecture: compute, storage, networking,
identity, and observability. Give 2 options (managed vs. self-hosted)
with cost, scaling, and failure-mode trade-offs.
Format with headings + a trade-off table.
TIPFollow up with "which single point of failure worries you most?"
10:00TERRAFORM02
Terraform / IaC
Produce a reviewable Terraform module with sane, secure-by-default variables.
main.tf
Act as a senior platform engineer. Write a [AWS / Azure / GCP]
Terraform module for [resource: e.g. VPC + EKS / AKS / GKE].
Parameterize [region, CIDR, node count, instance type] as variables.
Use remote state, tags, least-privilege IAM, and encrypted storage.
Add outputs for downstream modules. No hardcoded secrets.
Output complete .tf files + a variables table.
TIPAlways run "terraform plan" and read the diff before apply — never let AI apply.
11:00KUBERNETES03
Kubernetes Manifests
Produce production-ready manifests — probes, limits, and security context set.
deploy.yaml
Act as a Kubernetes engineer. Generate manifests to run
[app/image] with [N] replicas.
Include Deployment, Service, and HPA. Set resource requests/limits,
liveness/readiness probes, a non-root securityContext, and
[ConfigMap / Secret refs]. Add an Ingress for [host].
Output as valid YAML with brief comments per block.
TIPAsk it to flag anything that would fail a "kubectl apply --dry-run".
12:00PIPELINE04
CI/CD Pipeline
Produce a build-test-deploy pipeline with gates and a safe rollout strategy.
pipeline.yml
Write a [GitHub Actions / GitLab CI / Azure Pipelines] pipeline for
[app] deploying to [target: ECS / AKS / Cloud Run].
Stages: lint → build → scan (SAST + image scan) → terraform plan →
deploy to staging → manual approval → prod. Cache deps, pin versions,
and inject secrets from [OIDC / secrets manager] — never inline.
Output the complete pipeline file + a stage summary.
TIPAdd "use OIDC, no long-lived cloud keys in the repo" every time.
Afternoon · Operate & Document14:00 – 17:00 · debug, automate, and write it down
14:00DEBUG05
Debug Cloud Errors
Produce a ranked diagnosis from an error or stack — likely cause, next checks.
debug.md
Act as an SRE. Here's an error from [service / CLI]:
"[paste error / kubectl describe / cloud event]".
Stack: [context: provider, resource, recent change].
Give a ranked list of likely causes, the exact command
to confirm each (CLI / kubectl / cloud console), and the fix.
Call out anything that needs a config or IAM change.
TIPPaste the full error AND what changed recently — the diff is usually the cause.
15:00SCRIPTING06
Automation Scripts
Produce idempotent CLI / scripts — AWS CLI, kubectl, or a tidy Python automation.
automate.sh
Act as a DevOps engineer. Write a [bash / Python] script to
[task: e.g. rotate keys / tag untagged resources / drain nodes].
Use [AWS CLI / az / gcloud / kubectl]. Make it idempotent,
add dry-run + confirm flags, handle pagination and errors,
and log each action. No credentials in the script.
Output complete, runnable code with usage comments.
TIP"Add a --dry-run that prints actions without making changes" prevents accidents.
16:00INCIDENT07
Log & Incident Triage
Produce a clean incident summary from messy logs and alerts.
incident.md
Turn these logs/alerts into an incident summary:
"[paste logs / CloudWatch / metrics]".
Include: title, severity, affected services, timeline,
probable root cause, blast radius, and immediate mitigation.
Separate "what we know" from "what we're assuming".
Format for [Slack / Jira / status page].
TIPAsk for a one-line summary on-callers can scan in 2 seconds.
17:00RUNBOOK08
Runbook & Review
Produce a repeatable runbook and a security review of the generated infra.
runbook.md
Review this [Terraform / manifest / pipeline]:
"[paste config]".
Flag insecure defaults, over-broad IAM, missing encryption,
public exposure, and drift risk. Then write a runbook
for [deploy / rollback / on-call response] with exact steps.
Output the review checklist + the runbook.
TIPEnd the day here — a runbook today saves a 3 AM scramble tomorrow.
/ GOLDEN RULE 01
Never paste secrets.
No keys, tokens, or .env contents in a prompt. Use placeholders and wire real secrets from a secrets manager.
/ GOLDEN RULE 02
Always review generated infra.
AI drafts; you verify. Read the terraform plan, check IAM scope, then own the apply.
Save it. Share it. Use it tomorrow.
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By the Cloud Career Lab team · AI & Cloud Engineering