balajij8
Esteemed Contributor II

‎08-17-2026 06:34 AM

Hi Kartik,

 
Conversational interface in Databricks Genie is the straightforward part - establishing the underlying semantic layer is where the real effort lies. Its fully dependent on the metadata, relationships and business context fed into the space.

Genie relies heavily on three core inputs to produce reliable SQL and excellent answers
  • Well Defined Metrics - Establishing which tables are authoritative, the exact calculation logic, default filters that must be applied and which dimensions are valid for slicing.
  • Explicit Relationships - Defining primary/foreign key relationships, explicit table grain and distinguishing canonical source tables from intermediate or derived views.
  • Domain Vocabulary - Codifying domain-specific terms (eg NPT or ROP), standardizing relative time windows (eg fiscal calendar vs calendar week vs trailing 7 days), and quantifying abstract concepts like performance.
Teams frequently struggle if the context is not centralized. It is typically fragmented across internal knowledge in inline comments in transformation pipelines, stale documentation and competing definitions across business units. Genie forces an organization to bring that knowledge into Unity Catalog table/column comments, Genie instructions, governed metric definitions and curated benchmark SQL queries. Natural language interface merely accelerates the delivery of incorrect answers without this foundation.

The valid path is to build it incrementally. Using Unity Catalog metric views with targeted Genie space instructions, isolate a single bounded domain initially, lock down the semantic models, test the query generation against all cases and then expand that blueprint across other domains. Ontology will bring a revolution in this area.