How do you prompt MontyCloud Conversations to get a client-ready report?
Include five components in every prompt: (1) the data source, (2) the scope, (3) the output format, (4) the constraints, and (5) the audience. A well-structured prompt returns a finished deliverable. An open-ended prompt returns a data dump.
Scenario
Maya is a senior sales engineer at Northbound MSP. One of her clients, a regional financial services firm, has a quarterly cost review on Friday. She has 30 MontyCloud-managed accounts in scope, and a director who wants something he can actually present in a 15-minute slot.
She opens MontyCloud Conversations and types what feels natural: “What are my cost savings?”
The platform comes back with a list. Numbers, services, accounts, all of it real, all of it pulled from the latest Cost Optimization findings. Maya scans the output. She has data. She does not have anything she can hand to her director.
So she opens a spreadsheet. She starts reformatting. By the time she’s filtered to the top opportunities, grouped them by tenant, and turned the whole thing into something that looks like an executive view, an hour has gone by. The deliverable is fine. That’s not a process she can repeat thirty times.
She thinks back to a session she ran the week before with a teammate. He’d asked Conversations a very different kind of question, one that came back already structured and ready to send. Maya had assumed it was a different feature. It was the same feature. He just asked differently.
The Gap Between a Question and a Deliverable
Most teams using MontyCloud Conversations prompt it like a chatbot, and it answers like one. That’s the gap between getting a data dump and getting something you can put in front of a customer.
Conversations isn’t running on generic cloud knowledge. It’s reasoning over your actual tenant telemetry: WAFR findings, cost patterns, compliance gaps across every account you’ve onboarded. The data is there. Whether you can pull a finished deliverable out of it is mostly a function of how you ask.
Compare the prompt Maya used with the one her teammate used:
“What are my cost savings?”
“Using the latest Cost Optimization findings from the WAFR assessment, identify the top five savings opportunities across all tenants in scope. Return results in a table with columns for Tenant, Service, Current Monthly Cost, Estimated Savings, and Recommended Action. Use only data present in the assessment. This output will be used in a customer-facing executive report.”
Same data. The first gets you a list of numbers. The second gets you something you can send. What follows is what the second prompt is doing differently.
How Do You Write a Prompt for MontyCloud Conversations? The Five-Component Framework
Every strong prompt in MontyCloud Conversations answers five questions:
What data should MontyCloud use?
What should it include, and what should it leave out?
What should the output look like?
What rules must it follow?
Who is it for?
1. Context. Be explicit about the source. “Using the latest WAFR assessment” gets you different output than “based on what you know about my environment.” The first names data the system can actually pull from. The second invites guessing.
2. Scope. Open prompts get you everything. Limit the output. “Top five findings only” or “Security pillar findings from the past 30 days” gives the system a boundary to work within.
3. Format. Probably the most underused lever in the whole framework. Specify the structure before the content. A Markdown table, an HTML report, a numbered task list, a Statement of Work. The format you ask for decides whether you can send the output directly or spend an hour reformatting it. (This is the one Maya missed.)
4. Constraints. Tell the system to use only data present in the assessment. Phrases like “do not infer or generate missing values” stop it from filling gaps with plausible-looking but fabricated metrics. Those metrics look fine until someone tries to act on them.
5. Purpose. The same findings should read differently in an executive summary than in an engineering task list. Naming the audience tells the system how technical to get. “For a customer-facing executive report” produces different output than “for the remediation team.”
Five MontyCloud Conversations Prompt Templates for MSPs
After her cost-review experience, Maya saved five prompts to Northbound’s shared team library. Each one is ready to paste into MontyCloud Conversations.
What’s a good prompt for an executive WAFR summary?
Using the latest WAFR assessment, generate an executive summary covering overall risk posture, top five risks by severity, and the business impact of each. Format as a short opening paragraph followed by a prioritized list. This is for senior stakeholders with no technical background. Keep the language clear, business-focused, and free of unexplained acronyms.
What’s a good prompt for an engineering remediation task list?
From the Security pillar findings in the latest WAFR assessment, generate a prioritized task list of the top ten remediation actions. For each task, include the finding it addresses, the specific resource or service involved, the estimated effort in hours, and the recommended action. Use only data present in the assessment. Format as a numbered list. This is for the engineering team who’ll execute the work.
What’s a good prompt for a customer Statement of Work?
Using the top five findings from the Reliability pillar of the latest WAFR assessment, generate a draft Statement of Work. For each item, include a plain-language description of the issue, the recommended remediation approach, estimated hours to complete, and cost justification. Don’t assume values not present in the data. This will be shared with a customer as a formal service proposal.
What’s a good prompt for a customer-ready cost optimization report?
(The one Maya wishes she’d used the first time.) Identify the top five cost savings opportunities from the latest Cost Optimization WAFR findings. Return results in an HTML table with columns for Tenant, Service, Resource, Current Monthly Cost, Estimated Monthly Savings, and Recommendation. Use only actual data. Don’t generate or assume missing values. This report is for a customer-facing cost review.
What’s a good prompt for security quick wins?
From the Security pillar findings, identify all remediation actions that can be completed in under four hours. For each, include the finding title, the affected resource, the recommended action, and the estimated time to complete. Prioritize by severity. Format as a task checklist. This is for an engineer with limited time this sprint.
How to Get Conversations to Write Prompts for You
You don’t have to write every prompt from scratch. Conversations can build them too. For any reporting task Maya runs more than once (monthly cost reviews, quarterly WAFRs, security posture summaries), she asks the system to write the structured prompt:
“Help me build a prompt that generates a customer-ready cost optimization report from the latest WAFR findings. Include structure, format specifications, and constraints to ensure data integrity.”
What comes back becomes a template. Refine it once, save it to a shared library, and every engineer on the team produces the same standard of deliverable. This is where prompt design compounds. A good prompt becomes an asset the team reuses, not a one-off Maya typed at 11 pm on a Thursday.
From Templates to Team Infrastructure
The bigger gain is systematizing this across the team.
A new engineer joins Northbound on Monday. By Tuesday, they’re producing executive-grade WAFR summaries, because the prompt to generate them already lives in the shared library. When a senior architect refines a cost optimization prompt, every other architect on the team has that improvement the same hour. For an MSP running dozens of accounts, this is the difference between consistent reporting across every engagement and quality that varies depending on which engineer happens to be assigned.
Align your templates to your core service lines (WAFR, MAP assessments, FinOps reviews, security audits) and each one becomes a repeatable workflow.
Keeping Your Prompt Library Clean
A prompt that worked perfectly in Q1 can produce noisy output by Q3, because the data shape changed, a new pillar was added, or a customer’s environment grew. Set a quarterly review cadence for the shared library. Retire what no longer matches the data. Promote the prompts that consistently land. The library is an asset; treat it like one.
Conclusion
The difference between using MontyCloud Conversations as a search engine and using it as a delivery engine is mostly the prompt. Done well, the output is something Maya can use as-is, whether she’s sending it to a client or handing it to her engineering team.
The data is already in your MontyCloud environment. The gap between what you’re getting from it now and what you could be getting comes down to how you prompt.
Pick the single most repetitive reporting task your team handles manually. Rewrite the request using the five-component framework above. Save the result as a shared template. Then run it.
The first time you send a client a finished deliverable that took almost no manual effort, the case for structured prompting makes itself.
Use the comments section to share the templates that have worked for your team.
Frequently Asked Questions
What is MontyCloud Conversations?
MontyCloud Conversations is an AI interface that reasons over your tenant telemetry, including WAFR findings, cost patterns, and compliance gaps across the AWS accounts you’ve onboarded to MontyCloud.
How do I write a good prompt for MontyCloud Conversations?
Specify the data source, the scope, the output format, the constraints, and the audience. For example: “Using the latest Cost Optimization WAFR findings, identify the top five savings opportunities. Return as an HTML table with Tenant, Service, Current Cost, Estimated Savings, and Recommendation. Use only actual data. This is for a customer-facing cost review.”
Why does MontyCloud Conversations return a list instead of a report?
Because the prompt asked for a list. Open prompts produce open output. Specifying format (table, Statement of Work, task checklist) before content is what turns a data response into a deliverable.
Can MontyCloud Conversations write its own prompts?
Yes. Ask Conversations to build a prompt for any recurring reporting task, then save the result to a shared team library as a reusable template.
How can MSPs standardize MontyCloud Conversations across a team?
Build a shared prompt library aligned to your core service lines (WAFR, MAP, FinOps, security audits). Review the library quarterly to retire prompts that no longer match your data shape.
About the author
Tony Bulding is a Principal Sales Engineer at MontyCloud. He works with MSPs and enterprise partners to operationalize MontyCloud Conversations and MontyCloud AI across their customer environments.