◆ Apache Airflow

Debug broken DAGs

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 taskShow me the error logs for the `inventory_sync` DAG that failed this…+
Show me the error logs for the `inventory_sync` DAG that failed this morning.
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 `product_recommendation_engine` DAG failed again overnight. Pull up the logs for the…+
The `product_recommendation_engine` DAG failed again overnight. Pull up the logs for the `feature_engineering` task from the last three failed runs, and also show me the configuration changes made to that DAG in the last week. I'm trying to see if a recent change is causing this recurring issue.
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_churn_prediction` DAG failed, and the logs for the `model_scoring` task are just…+
The `customer_churn_prediction` DAG failed, and I can't pinpoint the exact cause from the logs alone.
The `customer_churn_prediction` DAG failed, and the logs for the `model_scoring` task are just showing a generic 'processing error'. I'm afraid we'll miss our weekly update to the sales team, and they'll lose faith in our data. I can't tell if it's a data quality issue in the input or a problem with the model itself. What's the most efficient way to get a more granular view of what went wrong inside that task without rerunning the entire DAG multiple times?
when the reply comes backPush once: ask it to sharpen the weakest part, and to say what it assumed. Helpful?
DBecome — change the patternI'm constantly spending hours sifting through huge log files to find the specific line that…+
I spend too much time digging through massive log files to find the root cause of DAG failures.
I'm constantly spending hours sifting through huge log files to find the specific line that caused a DAG to fail. It's incredibly inefficient and delays getting data to stakeholders. What habit can I change to more quickly identify the root cause of a failure, especially for complex DAGs with many tasks, so I can fix it faster?
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.