◆ Apache Airflow

Troubleshoot common Airflow problems

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 taskDiagnose why the 'daily_sales_report' DAG failed at 2 AM. Check the logs for the…+
Diagnose why the 'daily_sales_report' DAG failed at 2 AM. Check the logs for the 'transform_data' task and provide the error message to Sarah in finance.
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 acceptThe 'customer_segmentation_pipeline' DAG is taking much longer than usual to complete,…+
The 'customer_segmentation_pipeline' DAG is taking much longer than usual to complete, impacting the marketing team's campaign launch. Analyze its recent runs, identify the bottleneck, and suggest specific optimizations that can get it back within its SLA.
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 'inventory_update_job' DAG is showing success, but the numbers in the database are clearly…+
The 'inventory_update_job' DAG is showing success, but the numbers in the database are clearly wrong, and I can't find any errors in the task logs.
The 'inventory_update_job' DAG is showing success, but the numbers in the database are clearly wrong, and I can't find any errors in the task logs, even in debug mode. The operations team is already escalating because stock levels are incorrect, and I'm afraid there's a silent data corruption issue that's hard to trace. If this isn't fixed before the next run, we could have major shipping errors. What's the likely diagnosis, and what's the best next move to uncover the actual problem?
when the reply comes backPush once: ask it to sharpen the weakest part, and to say what it assumed. Helpful?
DBecome — change the patternI spend too much time manually sifting through logs and restarting DAGs when minor issues…+
I spend too much time manually sifting through logs and restarting DAGs when minor issues arise.
I spend too much time manually sifting through logs and restarting DAGs when minor issues arise, which pulls me away from strategic work and delays data availability for the business. This reactive firefighting is exhausting. What habit should I change to build more resilient DAGs and better tooling that allows for quicker, more automated diagnosis and recovery, reducing my constant involvement?
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.