You paste the error. You get a pandas answer. You are running PySpark.
Nothing about the response was wrong. It was correct, well explained, and solved a problem you do not have. You spend twenty minutes finding that out, and the twenty minutes feel like your fault.
They are not your fault, exactly. But they are avoidable, and the fix takes four lines.
🔍 What actually happened
When you paste a bare error message, a lot of information is missing. Which engine you are on. Which version. Whether this runs in a notebook, an orchestrator, or a scheduled job. What the data looks like.
An answer still has to be produced, so the gaps get filled with the most common case. Most Python data questions on the internet are pandas questions, so you get a pandas answer. Most SQL questions are Postgres questions, so you get Postgres syntax that Redshift will reject.
Research on this failure describes it precisely: models pick functions that do a similar job but belong to a different situation. The result is correct as code and wrong for your system.
There is a version of this that is harder to catch. Ask about a library you really do use, and you can get a function that existed two releases ago. Studies of deprecated API use in generated code found that models suggest outdated functions without saying which version they are assuming. One analysis of agent-written pull requests found that 75% specified a version and described this as better than ordinary chat usage, where versions are rarely mentioned at all.
The reason this one is worse: deprecation warnings are usually soft. Code using a deprecated function often keeps working for a release or two, so it passes your tests, gets merged, and fails months later during an upgrade.
And at the far end, roughly one in five packages suggested by language models does not exist anywhere. There is now an attack built on this. People watch for package names that models invent regularly, then publish real malicious packages under those names. So a made-up import is not always an import error. Sometimes it installs.
🧠 Why this hits beginners specifically
The obvious answer is that beginners do not include their setup. True, but that is a description, not a reason.
The reason is that you cannot tell which parts of your setup matter until you have been caught out by them. An experienced engineer mentions the engine automatically because they lost an afternoon to a Postgres answer in a Redshift warehouse. They are not being thorough. They are avoiding a specific memory.
Without that memory, everything about your setup looks equally relevant, which means it all looks equally skippable. You are not omitting the two facts that mattered. You are omitting all twelve, and two of them happened to matter.
There is a second reason, and it is about what you can check. A senior looks at an unfamiliar function and something feels wrong, because they have a rough sense of what exists in their engine. Earlier in your career there is no sense to compare against, so a made-up function and a real one look identical until you run them.
✅ Four lines, pasted every time
Put this above the error, every time:
Engine: Spark 3.5 on Databricks
Runs as: scheduled job, orchestrated by Airflow
Data: ~40M rows/day, JSON from a third-party API, schema changes without notice
Constraint: cannot add new cluster libraries
Four lines. Notice what they are doing.
Engine and version is the one that stops pandas answers reaching PySpark problems, and the one that catches deprecated functions. State the version, not just the tool. "Spark" and "Spark 3.5" are different questions.
Where it runs changes the answer more than people expect. A fix that is fine in a notebook may be wrong in a scheduled job, because a notebook has you sitting in front of it and a job at 2am does not.
What the data looks like matters more than what type it is. Research on test generation found that describing how something behaves improved results substantially more than describing how it is built. "JSON that changes shape without warning" is more useful than "JSON."
One hard constraint, if you have one. This is the line that stops you receiving an answer you cannot use.
This is not the same thing as stating your row counts and latency budget. That matters too, and it is a different problem for a different post. This block is about what your system is. That block is about how much it has to handle.
⚖️ The objection worth taking seriously
The weak version is "surely it can work out the stack from the error." Sometimes yes, and when it cannot, it will not tell you. It will answer confidently for the stack it assumed.
The stronger objection is about the habit itself. A block you paste every time is a block you stop reading, and a stale block is worse than no block at all. If you move to Spark 4 and your saved snippet still says 3.5, you have not just failed to help. You have actively misled, with your own credibility behind the wrong information. Every answer will now be confidently wrong about your environment for a reason you supplied.
I think that is right, and it is the reason for keeping this to four lines. Four lines can be re-read in three seconds. A twenty-line template cannot, so it becomes furniture, and furniture goes stale.
Two habits follow from that. Re-read the block before sending, every time, which four lines makes possible. And treat any change to your environment as a change to the block, in the same commit if you can.
There is one more thing worth being honest about. This block reduces a class of failure. It does not remove it. A study in 2025 found the newest models hallucinating in up to 20% of tasks, some of them with more context rather than less. So verify unfamiliar functions against your own documentation whatever your prompt looked like.
🎯 The takeaway
Four lines, above every question:
1. Engine and version. The version, not just the name.
2. Where it runs. Notebook, scheduled job, or something with a person watching.
3. What the data does. How it behaves, not just its type.
4. One hard constraint. If there is one.
Re-read them before you send. That is why there are only four.
The answer you got was not wrong. It was right about a system that is not yours, and nothing in it told you which one it had in mind.
