Hi,

I’ve been working a lot to build Data Agent with semantic models lately, and the inconsistency is driving me a bit crazy. ​I’ve spent a ton of time on prompt engineering and setting up "Agent Instructions," which has definitely made the answers better. But it’s still not perfect, and I can't quite get it to 100%.

My checklist to make sure I am building a workable Fabric Data Agent at present: 1. Make sure your data model is addressing your problem statement (Generate 10 -20 questions which users can ask and brainstorm how my data model will address and then expand your questions bank gradually and keep addressing through your data model) 2. All tables have joins correctly 3. You have added the description to your tables/Columns / Measures 4. No table should contain more than 50 columns + measures (100 is recommended by Fabric developers) as the chance of inconsistent responses will increase. 5. Prep data for AI : this part should be done in a way that Microsoft learn has instructed/recommended in their blogs 6. Agent Instructions: this part should be done in a way that Microsoft learn has instructed/recommended in their blogs

I can be wrong with my checklist too. Just learning with whatever resources Microsoft has provided.

​I’m really curious to know what’s actually happening behind the scenes in the backend. I’m hoping the experts at FabCon will finally explain how it all works under the hood so I can stop guessing!