◆ Alteryx

Remove columns with specific names

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 Alteryx tasks

The same task, four prompts

today's deadline · the next reviewer · the stuck moment · the pattern
AExecute — do the immediate taskRemove the 'Internal_Notes', 'Legacy_ID', and 'Audit_Trail' columns from the 'Client_Data'…+
Remove the 'Internal_Notes', 'Legacy_ID', and 'Audit_Trail' columns from the 'Client_Data' dataset before it's sent to the marketing team.
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 remove these columns from the 'Sales_Leads' dataset for the external vendor, review…+
Before I remove these columns from the 'Sales_Leads' dataset for the external vendor, review the list of columns to be removed. Flag any columns that might be critical for future analysis or compliance, suggest if any should be archived instead of deleted, and ensure the remaining columns align with the vendor's data requirements.
when the reply comes backPush once: ask it to sharpen the weakest part, and to say what it assumed. Helpful?
CDecide — diagnose the stuck momentI've been asked to remove 'PII' columns from a dataset before it goes to a new analytics…+
I've been asked to remove 'PII' columns from a dataset, but I'm unsure if I've identified all of them.
I've been asked to remove 'PII' columns from a dataset before it goes to a new analytics platform, but I'm unsure if I've identified all of them. The legal team is very strict about this, and I'm afraid of a compliance violation. Some column names are ambiguous, like 'User_Info', and I don't have time to manually inspect every row of this massive dataset. What's the best way to quickly cross-reference common PII indicators against the column names and flag any potential misses?
when the reply comes backPush once: ask it to sharpen the weakest part, and to say what it assumed. Helpful?
DBecome — change the patternI constantly spend time manually identifying and removing sensitive or irrelevant columns from…+
I constantly spend time manually identifying and removing sensitive or irrelevant columns from datasets.
I constantly spend time manually identifying and removing sensitive or irrelevant columns from datasets before sharing them or using them for analysis. This is a repetitive task that feels prone to human error, and I worry about accidentally sharing sensitive information. What habit should I change in my data preparation workflow to automate or streamline this column removal process, especially for recurring reports?
when the reply comes backPush once: ask it to sharpen the weakest part, and to say what it assumed. Helpful?

Questions people actually ask

honest answers, no sign-up

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 Alteryx'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.