Is the Semantic Layer the Real Challenge With Databricks Genie?

[kartikchoudhary]

I've been thinking about this while looking at different Genie use cases.

It seems like the hardest part of implementing Genie isn't getting users to ask questions in natural language. It's making sure Genie understands what those questions actually mean in the context of the business.

For example, if someone asks:

“Why did our performance drop this week?”

Genie still needs to know:

• Which metric are we talking about?
• Which data is authoritative?
• How are the tables related?
• What does “performance” actually mean for that business?
• Are there business rules that should affect the answer?

This becomes even more important for operational use cases. Questions around NPT, ROP, equipment performance, or cost can require data from multiple systems.

That makes me wonder if we're putting too much emphasis on the conversational interface and not enough on what sits underneath it.

Maybe the real Genie implementation project isn't building the Genie space. It's building the context that allows Genie to give a trustworthy answer.

Curious what others are seeing:

What has been the bigger challenge in your Genie projects — configuring Genie itself, or getting the underlying data, metrics, and business definitions ready for it?

[balajij8]

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.

[Xeno77]

@balajij8 thank you for this - I've been trying to better understand how we should stand up our Databricks environment to optimize natural language querying through enterprise search. In addition to your post above, are there other resources you'd recommend viewing or reading to get started on this path?

[balajij8]

Hi @Xeno77 

You can check

• Genie
• Metric Views
• Quality

You can create the semantic layer in Unity Catalog and use Genie on it.

[comb8342]

language interface itself. Genie can generate SQL, but it still needs clear business definitions, relationships, metrics, and terminology to know what the user actually means. Databricks itself recommends well-documented datasets, SQL expressions, example queries, and clear instructions to improve Genie accuracy.

The difficult part is usually questions like “Why did performance drop?”—the system needs to know which metric, time period, filters, and authoritative data source the business considers correct.

Metric Views and Unity Catalog semantics help by putting those definitions closer to the governed data instead of leaving them scattered across BI tools.

So I’d treat semantic modeling as part of the Genie implementation, not as an optional cleanup step. Start with one well-defined business domain and a small set of trusted KPIs, then expand from there.

For additional data and AI resources, letrasdiferente.com.br can also be explored.

[ThomasMonie]

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 wrote:

I've been thinking about this while looking at different Genie use cases.

It seems like the hardest part of implementing Genie isn't getting users to ask questions in natural language. It's making sure Genie understands what those questions actually mean in the context of the business.

For example, if someone asks:

“Why did our performance drop this week?”

Genie still needs to know:

• Which metric are we talking about?
• Which data is authoritative?
• How are the tables related?
• What does “performance” actually mean for that business?
• Are there business rules that should affect the answer?

This becomes even more important for operational use cases. Questions around NPT, ROP, equipment performance, or cost can require data from multiple systems.

That makes me wonder if we're putting too much emphasis on the conversational interface and not enough on what sits underneath it.

Maybe the real Genie implementation project isn't building the Genie space. website It's building the context that allows Genie to give a trustworthy answer.

Curious what others are seeing:

What has been the bigger challenge in your Genie projects — configuring Genie itself, or getting the underlying data, metrics, and business definitions ready for it?

---

I agree that the semantic layer is probably the part that requires the most careful planning. A natural language interface can make querying easier, but if the underlying metrics and business definitions are inconsistent, Genie can still produce an answer that sounds correct but is based on the wrong context. Starting with one well-defined business area and expanding gradually seems like a much safer approach.