Configuring Genie: practical steps for more reliable answers

[arthurfr23]

I published a hands-on guide to preparing and testing a Genie Agent, using a small sales dataset with known results

A few practices covered:

• Define table grain and relationships to avoid inflated totals after joins.

• Document metric definitions, currencies, and date semantics.

• Add reviewed SQL examples for recurring questions.

• Create benchmarks with expected results and rerun them after configuration changes.

The article includes SQL setup, sample instructions, and validation scenarios.

https://medium.com/@arthurfr23/databricks-genie-a-practical-guide-from-tables-to-tested-answers-aa91dff98587 

What has been your biggest challenge with Genie: business context, joins, or validating answers?

[Khasim_1]

Great guide, Arthur! I particularly agree with your point on defining table grain and relationships—those are often the silent killers of 'Genie' accuracy, especially when joins inflate totals. In my current work with complex medallion architectures, the biggest hurdle hasn't just been the join logic, but ensuring the 'semantic layer' remains consistent as business definitions evolve. Do you have a preferred method for managing 'Gold' layer metric definitions so that the Genie agent doesn't drift when the underlying business logic changes?