◆ Apache Airflow

Troubleshoot common Airflow issues

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 taskThe 'customer_segmentation' DAG failed on its last run. Check the logs and restart it once…+
The 'customer_segmentation' DAG failed on its last run. Check the logs and restart it once you've identified the issue. It needs to complete by end of day.
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' DAG failed again. Before restarting it, can you analyze the failure…+
The 'customer_segmentation' DAG failed again. Before restarting it, can you analyze the failure pattern? It seems to be related to upstream data quality issues from the CRM system. Can you pinpoint the exact data anomaly that caused this and flag it for the data source owner before we rerun it?
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' DAG is showing as running, but no new data has appeared in the warehouse…+
The 'inventory_update' DAG is showing as running, but no new data has appeared in the warehouse for hours.
The 'inventory_update' DAG is showing as running, but no new data has appeared in the warehouse for hours, and the business intelligence team is asking where their updated stock levels are. I can't see any errors in the logs, but it's clearly stuck. I'm afraid to just kill it and restart, in case it corrupts something. Is it a deadlock, a resource contention issue, or something else entirely? What's the safest way to get it moving again without risking data integrity for the e-commerce site?
when the reply comes backPush once: ask it to sharpen the weakest part, and to say what it assumed. Helpful?
DBecome — change the patternWe spend too much time manually sifting through logs to diagnose DAG failures, especially when…+
We spend too much time manually sifting through logs to diagnose DAG failures.
We spend too much time manually sifting through logs to diagnose DAG failures, especially when they're intermittent or silent. This constantly delays our response to critical data issues and frustrates our data consumers. What habit can we change to more quickly identify the root cause of failures, perhaps by surfacing key error patterns or anomalies, so we can resolve them faster and maintain trust?
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.