◆ Microsoft SQL Server

Implement date range rolling sum

This is real work, not a feature someone invented — it comes from real job ads and real questions people asked. Below are four ready AI prompts: get it done, make it easy for the next person to say yes to, work out the right move when you are stuck, and stop it coming back.

4prompts
See the prompt↓All Microsoft SQL Server tasks

The same task, four prompts

today's deadline · the next reviewer · the stuck moment · the pattern
AExecute — do the immediate taskCalculate a 30-day rolling sum of 'SalesAmount' for each 'CustomerID' in the…+
Calculate a 30-day rolling sum of 'SalesAmount' for each 'CustomerID' in the 'Sales.Transactions' table, ordered by 'TransactionDate'.
when the reply comes backPush once: ask it to sharpen the weakest part, and to say what it assumed. Helpful?
BImprove — make it easier to acceptBefore I present this rolling sum to the marketing team for their campaign analysis, make sure…+
Before I present this rolling sum to the marketing team for their campaign analysis, make sure it accurately reflects a full 30-day window even at the start of a customer's history, and handles any gaps in transaction dates without skewing the average.
when the reply comes backPush once: ask it to sharpen the weakest part, and to say what it assumed. Helpful?
CDecide — diagnose the stuck momentThe 30-day rolling sum for new customers in the 'Sales.Transactions' table appears artificially…+
The rolling sum for new customers looks too high, and I'm worried it's misrepresenting their initial spend patterns.
The 30-day rolling sum for new customers in the 'Sales.Transactions' table appears artificially inflated during their first month. I'm afraid this will lead the marketing team to misallocate budget for new customer acquisition. I can't tell if it's including partial periods incorrectly or if the window function needs adjustment for sparse data. What's the likely issue, and what's the best way to get accurate early-period rolling sums for these customers?
when the reply comes backPush once: ask it to sharpen the weakest part, and to say what it assumed. Helpful?
DBecome — change the patternI consistently face challenges in creating accurate rolling calculations, especially when…+
I frequently struggle to implement accurate rolling calculations that handle edge cases like sparse data or initial periods.
I consistently face challenges in creating accurate rolling calculations, especially when dealing with sparse data or the initial periods of a time series. This often leads to rework and delays in delivering insights to the business analysts. What habit can I change to design more robust rolling sum queries from the outset, accounting for these common pitfalls?
when the reply comes backPush once: ask it to sharpen the weakest part, and to say what it assumed. Helpful?

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Every task here was seen in the real world. Someone doing the job named it, a real job ad asked for it, or a lot of people asked about it online.

If nothing real showed a task, it is not on the page. That is the whole rule.

They are the same job approached four ways, because what you need depends on where you are.

Get it done today. Make it easy for the next person to say yes to. Work out the right move when you are stuck. Learn the pattern so the job stops coming back.

For most of these jobs it can carry the heavy thinking - draft it, sort it, check it, rehearse it with you.

It cannot sit in your chair, take the blame when a number is wrong, or notice what nobody wrote down. Let it do the first 80%. Keep the last 20% that is truly yours.

No. Copy any prompt and paste it into the AI you already use. No account, no score, no wall in the way.

Any of them. The prompts describe the work rather than naming a product, so they are not tied to one assistant.

That is also why they keep working when you switch.

Change it freely. Every prompt is a starting line, not a rule.

Put in your real numbers, your real names and your real deadline. The more you make it yours, the better the answer comes back.

The tasks come from real job ads, published job data and the questions people ask in public forums.

The steps come from Microsoft SQL Server's own documentation, with practitioner sources for the traps the manual does not mention.

Push once. Ask it to sharpen the weakest part and to say what it assumed.

Most wrong answers come from a missing detail rather than a bad prompt - tell it the thing it could not know.