◆ Apache Airflow

Get exception details on on_failure_callback context

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

The same task, four prompts

today's deadline · the next reviewer · the stuck moment · the pattern
AExecute — do the immediate taskWhen the customer data import fails, I need the on_failure_callback to include the full…+
When the customer data import fails, I need the on_failure_callback to include the full exception details. The support team needs this to troubleshoot quickly.
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 deploy this change to the customer data import pipeline, I need to verify that the…+
Before I deploy this change to the customer data import pipeline, I need to verify that the on_failure_callback captures and includes the complete exception details. The support team relies on this information for immediate troubleshooting, and without it, they'll be blind to the root cause, delaying customer resolutions.
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 customer data import failed this morning, but the on_failure_callback only gave a generic…+
The customer data import failed, but the on_failure_callback only gave a generic error message.
The customer data import failed this morning, but the on_failure_callback only gave a generic error message, not the full exception details. The support team is already asking for more information, and I can't tell if I misconfigured the callback or if there's a limitation. I'm afraid this will delay us from fixing the data for our buyers. What's the most likely diagnosis and what's the best next move to ensure the full exception details are captured?
when the reply comes backPush once: ask it to sharpen the weakest part, and to say what it assumed. Helpful?
DBecome — change the patternI frequently get incomplete exception details from on_failure_callbacks, which slows down…+
I frequently get incomplete exception details from on_failure_callbacks, slowing down incident resolution.
I frequently get incomplete exception details from on_failure_callbacks, which slows down incident resolution and frustrates the support team. This makes us seem unresponsive. What habit can I change to consistently ensure that on_failure_callbacks capture comprehensive exception details, empowering faster troubleshooting?
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 Apache Airflow'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.