[blackbear_]

You could try to give a challenge that has a few hidden gotchas, and discard candidates that do not spot them. How to do this depends on the role you are hiring for.

For example, in our data scientist interviews we also candidates to analyze datasets with imbalanced classes, outliers, correlated samples, etc. Correctly dealing with these issues requires particular techniques, and most importantly the candidate has to explicitly check whether these issues are present or not. Those who use LLMs mindlessly will not even realize this is the case.

[calabin]

I like this - a huge part of our engineering work is ETL pipelines so giving them some data to process makes things a lot harder to fake.