In my previous article, I wrote an article about building a strong foundation for Databricks Genie Spaces through data modeling, metadata, and semantic design. This time, I want to share a few lessons from running a Genie Space against a real healthcare analytics use case.
The biggest surprise wasn't AI. It was how quickly Genie exposed problems that already existed in the data.
When we first deployed the Genie Space and ran it against our benchmark questions, the results were far from perfect. The out-of-the-box configuration answered only 6 out of 20 benchmark questions correctly. Even after multiple rounds of tuning, a repurposed legacy analytics dataset for BI workloads never achieved more than 75% benchmark accuracy.
What changed everything wasn't prompt engineering. It was dataset design.
Once we defined and built a live delta dataset with pre-defined metrics specifically for Genie, removed irrelevant columns, simplified business logic, standardized categorical values, and aligned the schema to the questions users were actually asking, benchmark accuracy eventually reached 100%.
- While many teams may focus on configuring Genie and fine-tuning prompt. For our team, the biggest boost in accuracy came from preparing the underlying data for Genie.
- Another lesson was the importance of benchmarking. Without benchmarks, every discussion becomes subjective.
- Quick Tip: Users may ask the same question in different ways. Databricks recommends using 2-4 different phrasings of the same question (but the same SQL code) to fully assess accuracy.
We also learned that not every configuration change improves accuracy. Below is a visual of our findings;
- Table descriptions helped significantly.
- General instructions provided some improvement.
- Surprisingly, adding large numbers of synonyms wasn't always beneficial and sometimes introduced additional ambiguity.
- The most effective configuration element by far was SQL examples.
- Quick Tip: Whenever a business rule involved non-trivial logic, such as event-level calculations, eligibility definitions, or classifications, SQL examples consistently outperformed additional instructions. Genie seemed to learn far more effectively from concrete examples than from lengthy explanations.
- Another important observation was that Genie is not entirely deterministic. The same question can occasionally be presented differently depending on formatting choices, percentage calculations, null handling, or interpretation of date ranges. We found ourselves spending as much time standardizing outputs as improving answers.
This is where governance becomes essential
A successful Genie Space needs more than good metadata. It needs agreed definitions, controlled business logic, benchmark testing, and a clear process for evaluating changes. Otherwise, the results erodes trust.
The teams that will get the most value from Genie Spaces and turn a demo into a production-ready analytical product won't be the ones writing the most sophisticated prompts. They'll be the ones investing in benchmark-driven development, semantic consistency, and Genie Tailored purpose-built datasets.
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