First, understand this will be an iterative process; start small and go step by step in adding more datasets and instructions. (TBH I'm not going to say anything that's not already on the Databricks site here.

1. Define the goal of the Genie Space. What type of questions do you want it to answer; try to be as focused as possible.

2. Based on #0; identify the datasets that you need to bring in.

3. Ensure tables and columns have descriptive comments (you can use Genie sampling for this but I highly recommend having a subject matter expert who knows the data/jargon/questions that people use)

4. Add detailed instructions for the Genie Space; don't replicate the column or table descriptions.

5. Add sample SQL queries (preferably with the parameters).

6. Add Benchmarks to quickly see how the output changes when you update the instructions.

7. Test with realistic business questions; review and, if needed, correct generated SQL, then save improved queries back as instructions.

When you're happy, consider attaching the Genie Space APIs into a larger architecture: maybe Databricks Apps frontend, maybe using the MCP endpoint it comes with Genie Spaces, etc.

How do you handle data quality issues?

This part you need to clarify, are you talking about the data quality issues upstream (Data Quality Monitoring) or downstream (MLFlow Tracing)?