The Top 10 AI Prompts to Manage Your Cloud.
Whether you ask ChatGPT, Claude, Claude Code, Cursor, Gemini, Llama, or Perplexity, the same ten prompt types do the heavy lifting in cloud operations, on AWS, Azure, and GCP alike. For each: what belongs in the prompt, the trap to avoid, and a ready-to-use example.
The List
Ten prompt types. Master these, master your cloud.
Ranked by how often they pay off. A prompt type is a reusable shape of question: what to include, what to demand in the answer, and the trap to avoid. Master the shape once and it pays off every week.
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The Morning Check
What to include: Ask for a ranked list, not a status dump. Give the LLM your scope (every account and region you can reach), what normal looks like for your environment, and how to rank what it finds (blast radius beats alphabetical). Without live access, paste yesterday's known state so it has a baseline.
> Scan all my accounts and tell me the three things that need attention before standup, ranked by blast radius.
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Resolving an Incident
What to include: Anchor to a time window and ask what changed, not what is broken. Name the failing service, the window, and every change source you want checked: deploys, config edits, feature flags, scaling events, expired credentials.
> payment-service is throwing 5xx errors. What changed in the hour before they started, and what else depends on it?
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Understanding a Cost Spike
What to include: Ask for attribution, not description. You already know spend went up; the prompt should demand which resource moved, what created it, which team or deploy it traces to, and the compare window that defines the spike.
> Spend jumped 18% this week versus last. Which resources drove it, who created them, and through what change?
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Finding Zombies
What to include: Idle is easy; safe-to-remove is the hard part. Ask for orphaned and idle resources together with the evidence to act: owner, creation story, what depends on each, and what breaks if it goes. Never ask for a delete list without a blast-radius check.
> Find resources with no traffic in 30+ days, tell me who owns each, and which are safe to remove.
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Auditing Access
What to include: Ask about effective permissions, not policy text. The prompt should force traversal: roles, policies, SCPs, permission boundaries, and role-assumption chains, plus indirect paths like lifecycle rules and policy edit rights.
> Who can actually delete data from our customer-data bucket, and through which path does each one get there?
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Tracking Change
What to include: Demand one timeline across every ledger: code deploys, console edits, IaC applies, config and flag changes, identity events. Ask for attribution (person or mechanism) and include the clean changes, because excluding non-problems fast is most of diagnosis.
> What changed in prod since Friday, who or what made each change, and which ones look unusual?
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Stress-Testing Capacity
What to include: Never ask "will it hold?". Ask what breaks first and at what multiplier. The prompt should walk every hop downstream of the headline service and check each component's actual configured limit: connection pools, caches, gateways, quotas, third-party rate limits.
> If checkout-service traffic triples next month, what breaks first, and at what multiplier does each limit cross?
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Finding Drift
What to include: Compare intent against reality and demand provenance. The prompt should check live state against IaC state, then explain how each drifted resource got there: console edit, retired CI role, agent-created, and rank by risk.
> Find production resources that aren't managed by Terraform and tell me how they got there.
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Pre-Flight for a Migration
What to include: Generic checklists miss the specifics that cause the incident. Ask for the sidecars, the alarms pinned to instance IDs, the instance profiles, and the hardcoded addresses attached to this service in this environment.
> I'm moving order-service from EC2 to ECS. What am I forgetting that is specific to this service?
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The Open Question
What to include: The highest-signal prompt type, and the hungriest for context. It needs history: baselines, near-misses, what was quietly concerning last week. Feed the LLM your recent incident summaries and current state, or use an agent that already carries them.
> What should I be worried about heading into the weekend that nobody has flagged?
Look at what every guidance note above has in common: scope, baselines, timelines, dependencies, provenance. Every prompt type lives or dies on context assembly, and hand-assembling context is the tax you pay on every single question. The teams that master these prompts fastest are the ones that stopped paying it.
FAQ
Frequently asked.
Do these prompts work with ChatGPT, Claude, Cursor, and other LLMs?
Yes. The ten types are model-agnostic: they work in ChatGPT, Claude, Claude Code, Cursor, Gemini, Llama, and Perplexity alike, because they describe what information the question needs, not how a specific model wants it phrased. The difference between models is smaller than the difference between a prompt with context and a prompt without it.
Do these prompts work for AWS, Azure, and GCP?
Yes. The examples use AWS vocabulary because it is the most common, but every type translates directly: swap CloudTrail for Azure Activity Log or GCP Cloud Audit Logs, IAM policies for Azure RBAC or GCP IAM, and the structure of the prompt stays identical.
What makes a cloud prompt actually work?
Three ingredients: scope (which accounts, regions, and services are in play), an anchor (a baseline, a time window, or a compare period), and a demand for attribution and ranking rather than description. A generic LLM needs all three pasted in fresh every session. An agent with a live context graph of your environment, like Oscar Ops, already has them, which is why the same question returns evidence instead of an essay.
Meet Oscar. The context is already assembled.
Oscar Ops is an AI cloud engineering agent that runs locally and carries your live Cloud Intelligence Graph™: scope, baselines, history, and dependencies built in. Every prompt type in this guide becomes one question with the evidence attached. It proposes; you approve. The Operator Edition is free.