u/DropMaterializedView

I made this video on some fun things that you can control if you use: TMDL Mastery: Accessing Power BI's Hidden Developer Settings

In case you want to completely skip over the video here is the summary of what was covered :

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WARNING: some of these are likely not officially supported as they are generated by PBI internally and so as a result could change at any time

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Custom Power Query Query Sort Order

annotation PBI_QueryOrder = ["Fact Table","Dim Date","Table"]

Analyze in Excel Drill thru for a measure (good SQL BI Article on this: https://www.sqlbi.com/articles/controlling-drillthrough-in-excel-pivottables-connected-to-power-bi-or-analysis-services/)

detailRowsDefinition =

SELECTCOLUMNS(

'Fact Table',

"Order Date", 'Fact Table'[OrderDate],

"Customer", 'Fact Table'[CustomerName],

"Amount", 'Fact Table'[order_val]

)

Date Column Join Behavior

joinOnDateBehavior

Custom Annotations

Enabling or disabling Time Intelligence

annotation __PBI_TimeIntelligenceEnabled = 1

Discouraging Implicit Measures

discourageImplicitMeasures: true

So long story short, I was playing around prepping a demo on semantic link labs and figured out you could:

• use semantic link labs to pull data out of a model
• Fabric Notebook AI functions to process the data and return it in JSON
• Semantic link labs to write the JSON results back to the model

I put together some fun examples in a notebook on my github here

Youtube video Demo here: https://youtu.be/-ky-JeH9bsI

Demos are as follows:

• Demo 1: Parsing SQL Tables from M Code
• Demo 2: Standard Power BI API to generate AI Measure Descriptions
• Demo 3: AI Measure Descriptions with a custom prompt
• Demo 4: Auto-Organizing Measures into Folders

I think a more involved real world use case of a pattern like this would be:

• having a notebook loop thru all the semantic models in a workspace / tenant
• use a custom prompt + similarity function to identify all the measures that should be the same + named the same definition
• Flag the deltas