◆ Apache Airflow

Troubleshoot Airflow task failures

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 taskInvestigate why the 'customer_segmentation_model' DAG failed last night. Check the logs for the…+
Investigate why the 'customer_segmentation_model' DAG failed last night. Check the logs for the 'train_model' task specifically, and tell me the exit code and any traceback. I need to know if it's a data issue or a code issue before our 10 AM standup.
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 dive deep into the 'customer_segmentation_model' DAG's failure, can you quickly…+
Before I dive deep into the 'customer_segmentation_model' DAG's failure, can you quickly summarize the most common reasons this specific DAG fails? Point me to the relevant log sections or metrics that would confirm those usual suspects, so I can narrow my focus.
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 'daily_reporting_pipeline' DAG failed with a 'MemoryError' today, but it ran perfectly…+
The 'daily_reporting_pipeline' DAG failed unexpectedly, and the logs are showing a 'MemoryError' but it ran fine yesterday.
The 'daily_reporting_pipeline' DAG failed with a 'MemoryError' today, but it ran perfectly yesterday. Our finance team is waiting on these reports. I'm worried a new data volume spike is silently killing it, or maybe a recent code change introduced a leak. I can't tell which without digging deep, and I'm on a tight deadline for the finance reports. What's the most likely cause, and what's the fastest way to confirm it?
when the reply comes backPush once: ask it to sharpen the weakest part, and to say what it assumed. Helpful?
DBecome — change the patternMy DAGs keep failing intermittently with resource errors, like memory or CPU, and it's always a…+
My DAGs keep failing intermittently with resource errors, and it's always a scramble to figure out why.
My DAGs keep failing intermittently with resource errors, like memory or CPU, and it's always a scramble to figure out if it's a code problem, a data volume spike, or just a noisy neighbor on our infrastructure. This wastes hours every week. What habit should I change to more quickly diagnose and prevent these kinds of intermittent resource-related failures across my pipelines?
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.