Understand Your Users Demands from Databricks Genie with Conversation Clustering
Databricks AI/BI Genie has replaced the reliance on dashboards that many data teams had with something much better for the user. Instead of hunting for the right dashboard, a business user just asks for the insight that they need. Genie then translates the question into SQL over governed Lakehouse data, runs it, and answers in plain language with the query and results attached. This is a much better and faster experience for business users, as it does not require waiting for an analyst to respond and create a chart.
However, like anything, it comes with its own challenges. The moment analytics become a conversation, you lose the aggregate view of what people are looking at.
A dashboard’s usage is legible at a glance, but ten thousand free-form questions typed into a Genie space are not. The demand for information is still there, but it’s scattered across individual conversations, and no one can examine ten thousand of them to ensure the quality of what Genie is outputting is reliable.
Conversation clustering in Monte Carlo gives teams that top-down view back.
What clustering shows you
Monte Carlo supports conversation clustering for Databricks Genie. The Clusters panel in the Conversations tab groups your Genie space’s conversations into named topics, with each cluster having a plain-language description of the intent behind it, its share of total traffic, and a conversation count. One click filters the list to just the conversations in that topic.
Monte Carlo treats a Genie space as a platform agent and surfaces each turn, including the question, the SQL Genie generated, a sample of the results, and the final answer. Clustering sits on top of that and organizes the questions into the handful of topics that actually describe how the space is used. The topics come from what your users typed, not from a taxonomy you had to define up front, and new conversations are sorted into topics as they arrive, so the picture stays current as demand shifts.
It’s the dashboard-estate legibility you gave up, now restored for a world where the “dashboard” is a conversation.
What you do with it
Check that the space is used the way you scoped it. Genie spaces are usually built per domain, for example a finance space, a sales space, or an ops space, with a semantic model and instructions tuned to that domain. Clustering is the fastest way to confirm the scope holds. If a finance space’s second-largest topic is a people-analytics question, that’s a signal that demand exists for a space you haven’t built yet, or even that your instructions need a boundary. Share-of-traffic tells you which of those to dedicate attention to first.
Turn vague answer-quality worries into a specific fix list. Genie’s answers depend on the semantic model, metric views, and instructions behind the space. When you can see topics alongside answer quality, a cluster with real volume and shaky relevance becomes a precise to-do: this category of question is underserved, and it’s worth X% of your traffic. That beats reworking a semantic model on a hunch. A large uncategorized share, likewise, tends to mean users are pushing the space toward questions it was never set up to handle. This is useful to know before they lose trust in Genie.
Point your review time where it pays off. Teams cannot read every conversation, and random sampling wastes their time. Clustering lets you pull representative threads from your biggest topics and evaluate the ones that represent the most real usage. That, in turn, is the natural place to aim Monte Carlo’s agent evaluation monitors, so quality checks land on the questions your users actually ask most.
See which topics are driving cost. Genie now bills on consumption, and conversations aren’t uniform. One analytically heavy topic, for example, can run far longer and burn far more than a stream of quick lookups. Because the conversation list carries duration and token counts next to the cluster label, you can see which kinds of questions are driving spend, not just that spend went up.
Build enablement around real demand. The topics your users already gravitate toward are the blueprint for example questions, documentation, and onboarding for the next cohort. Instead of guessing what to seed a new space with, you can seed it with what people in a comparable space actually ask.
No instrumentation, data stays put
Genie keeps its conversation history behind its API rather than in a table, so Monte Carlo bridges that with a lightweight, fully managed collector that runs inside your Databricks workspace.
This means no code changes to your agents, no SDK, and nothing to instrument. Conversations flow in, get classified into topics, and the view updates as new ones land.
Everything stays governed inside your Lakehouse under Unity Catalog, with the access controls you already enforce.
Genie made talking to data effortless. Clustering makes understanding what the whole business is asking just as effortless, which is the part you’ll need as more of your users start with a question instead of a dashboard.
Want the top-down view of your Genie spaces? See Agent Observability for Databricks →
