Ask a room of cloud engineers how they use AI day to day and you will hear the same pattern: everyone pastes things into an LLM, nobody is quite sure they are asking well. The models change, ChatGPT, Claude, Claude Code, Cursor, Gemini, Llama, Perplexity, but the questions that matter in cloud operations do not.
That observation turned into the first guide in the OpsCanvas Academy: The Top 10 AI Prompts to Manage Your Cloud. Free, open, and tool-agnostic, it covers the ten question shapes that do the heavy lifting on AWS, Azure, and GCP alike, each with guidance on what to include and a ready-to-use example.
Why prompt types beat prompts
Copy-paste prompt lists age badly. They are written for someone else’s environment, someone else’s incident, someone else’s billing surprise. A prompt type is more durable: it is the reusable shape of a question, what information it needs, what to demand in the answer, and the trap that makes most people ask it badly.
Good cloud prompts turn out to share one anatomy regardless of the model: 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 instead of description. Most bad answers trace back to a missing ingredient, not a weak model.
A preview: the top three
The Morning Check. Ask for a ranked list, not a status dump: “Scan all my accounts and tell me the three things that need attention before standup, ranked by blast radius.” The trap is accepting a wall of green, which reports thresholds instead of deviations from your own baseline.
Resolving an Incident. Anchor to a time window and ask what changed, not what is broken: “payment-service is throwing 5xx errors. What changed in the hour before they started, and what else depends on it?” The trap is staring at the failing service’s logs when the cause is a change two hops upstream, a pattern we covered in depth in our 5xx root cause post.
Understanding a Cost Spike. Ask for attribution, not description: “Spend jumped 18% this week versus last. Which resources drove it, who created them, and through what change?” The trap is settling for a chart-shaped answer that names no resource and no owner.
The remaining seven cover zombies, access audits, change tracking, capacity stress-tests, drift, migration pre-flights, and the open-ended “what should I be worried about?”, all in the full guide.
The pattern, and the tax
Read the guidance for all ten and one thing repeats: scope, baselines, timelines, dependencies, provenance. Every prompt type lives or dies on context assembly, and with a generic LLM you assemble that context by hand, fresh, every session. That is the tax on every question, and it is why the same prompt returns gold for one engineer and generic advice for another.
You can pay the tax with discipline, and the guide shows you how. Or you can use an AI cloud engineering agent that carries the context permanently in a live Cloud Intelligence Graph™, which turns every type on the list into a one-line question with the evidence attached.
Either way, start with the guide, pick the two types you ask most often, and notice how different the answers get when the question carries its context.
Key Takeaways
Key points
- ✓A prompt type is a reusable shape of question: what to include, what to demand in the answer, and the trap to avoid.
- ✓The same ten types do the heavy lifting whichever LLM you use (ChatGPT, Claude, Cursor, Gemini) and whichever cloud you run.
- ✗The common failure is asking for description when you need attribution, or a list when you need blast radius.
- ✓The full guide, with guidance and a ready-to-use example for all ten, is free in the new OpsCanvas Academy.