◆ Apache Airflow

Write DAGs following best practices

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
See the prompt↓All Apache Airflow tasks

The same task, four prompts

today's deadline · the next reviewer · the stuck moment · the pattern
AExecute — do the immediate taskWrite a DAG to extract daily order data from the e-commerce database, transform it by…+
Write a DAG to extract daily order data from the e-commerce database, transform it by calculating total sales per product, and load it into the data warehouse for the business intelligence team.
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 delivering the new order data DAG, ensure it follows our best practices for modularity,…+
Before delivering the new order data DAG, ensure it follows our best practices for modularity, idempotency, and error handling. Include comprehensive logging for each task and clear documentation for future maintainers, so the business intelligence team gets reliable data and future debugging is straightforward.
when the reply comes backPush once: ask it to sharpen the weakest part, and to say what it assumed. Helpful?
CDecide — diagnose the stuck momentI've just written a new DAG to track campaign performance, but I'm concerned it might be too…+
I just finished writing a new DAG for campaign tracking, but I'm worried about its performance.
I've just written a new DAG to track campaign performance, but I'm concerned it might be too resource-intensive when it scales to millions of events. I've tried to optimize the SQL queries, but I'm not confident I've chosen the most efficient operators or parallelization strategy. The marketing team needs these insights quickly, and I don't want to crash the cluster. What's the likely diagnosis for performance bottlenecks in a new, high-volume DAG, and what's the best next move to optimize it without extensive refactoring?
when the reply comes backPush once: ask it to sharpen the weakest part, and to say what it assumed. Helpful?
DBecome — change the patternI've noticed that many of our DAGs, especially older ones, are incredibly complex and hard to…+
Our DAGs often become complex and difficult to understand or modify over time.
I've noticed that many of our DAGs, especially older ones, are incredibly complex and hard to read, making modifications or debugging a painful process for new engineers. This leads to errors and delays in getting new data to our stakeholders. What habit should I change in how I write DAGs from the start to ensure they remain clear, maintainable, and easily understandable for anyone on the team, even years down the line?
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.