How to Train ChatGPT on Your Power BI Data Model Using DAX Studio

Posted by

September 17, 2026

On January 27, 2026

Introduction

In modern business intelligence, Power BI has become a go-to platform for turning raw data into actionable insights. Meanwhile, ChatGPT and similar AI tools are transforming how we work with data, especially when it comes to writing complex DAX (Data Analysis Expressions) formulas. But here’s the catch: AI can only write powerful, accurate DAX if it understands your data model.

That is where DAX Studio steps in. By pulling your model metadata out and inputting it into ChatGPT, you can “train” the AI on your tables, measures, and relationships specifically, enabling it to create context-specific DAX formulas, optimize query performance, and aid in wiser report building. To install and download the DAX studio follow our guide: Getting started with DAX studio.

In this step-by-step guide, you’ll learn how to train ChatGPT on your Power BI data model using DAX formulas, enhance query performance, and facilitate wiser report building.

Why Train AI on Your Power BI Model?

AI tools like ChatGPT are extremely good for code generation. But without understanding your model, they’re essentially guessing. For example, if you have a table called Sales Transactions with a measure called Total Sales, ChatGPT won’t know anything about it unless you’ve provided the metadata. By giving AI a structured view of your model:

You get more accurate DAX recommendations.
You save yourself from trial-and-error and therefore development time.
You improve your query context, and AI will know how your tables and relationships play with each other.
You upgrade your Power BI performance tuning process.

Prerequisites

Before starting, make sure you have:

A published or local Power BI report (.pbix) file.
DAX Studio installed and associated with your model.
A ChatGPT or similar AI tool that accepts structured text input.
A good understanding of your model structure (tables, relationships, and measures).

Step 1: Connect DAX Studio to Your Power BI Model

Launch DAX Studio directly from your system:

Open Store Sales.pbix.
Click on the DAX Studio shortcut on your Desktop.

A pop-up menu appears.

Select the check box to the left of “PBI / SSDT Model”.
Select your Power BI file from drop-down.
Click

Step 2: Export the Data Model Metadata

Now, we’ll export the structure of our model, not the actual data, just the metadata.

In DAX Studio, go to Advanced → Export Metrics.

Save the .vpax file to your computer. This file contains the structure of your model, i.e., tables, columns, relationships, and measures, etc.

STEP 3: Prepare the Metadata for ChatGPT

To help ChatGPT “understand” your model:

Open your .vpax file using the import metrics option in DAX Studio.

Step 4: Train ChatGPT with the Metadata

You can now write something like:

“Here is my Power BI data model structure:

### Tables:

– Sales, Products, Time, Store

### Columns:

– Sales[Amount] (Decimal), Sales[Date] (Date), Sales[ProductID] (Integer)

– Products[ProductID] (Integer), Products[Category] (String)

– Time[Date] (Date), Time[Year] (Integer), Time[Month] (String)

– Store[StoreID] (Integer), Store[Location] (String)

### Measures:

– Total Sales = SUM(Sales[Amount])

– Avg Sales = AVERAGE(Sales[Amount])

– Product Count = COUNTROWS(Products)

### Relationships:

– Sales[ProductID] → Products[ProductID] (Many-to-One)

– Sales[Date] → Time[Date] (Many-to-One)

– Sales[StoreID] → Store[StoreID] (Many-to-One)

Can you help me with writing DAX formulas, optimizing performance, or understanding the relationships based on this model?”

This prompt allows ChatGPT to provide you with DAX suggestions, measure optimizations, or documentation specific to your model.

Step 5: Request AI for Advanced DAX or Optimization

ChatGPT can assist with:

Measurement creation, i.e., YoY growth, rolling averages, customer segmentation.
Performance tuning, for example, proposing alternative DAX patterns.
Model design enhancements, highlighting possible relationships to be improved.
Documentation, creating human readable model descriptions.

Sample prompt:

“Write a measure to compute the average order value for the past 90 days based on the model.”

AI response could be:

Avg Order Value (Last 90 Days) =
VAR Last90Days =
    DATESINPERIOD ( Time[Date], MAX ( Time[Date] ), -90, DAY )
RETURN
    DIVIDE (
        CALCULATE ( [Total Sales], Last90Days ),
        CALCULATE ( COUNTROWS ( Sales ), Last90Days )

)

Key Takeaways

Export your Power BI model metadata using DAX Studio.
Clean the metadata for AI-readability.
Fed into ChatGPT to generate context-aware prompts.
AI is not merely for equations, but for more intelligent BI creation.

Conclusion

Training ChatGPT on your Power BI data model is like giving it a map before asking for directions. Instead of generic formulas, you’ll get a highly accurate, context-aware DAX code that saves you time and boosts your reporting quality.

With DAX Studio as your bridge, AI becomes a powerful development partner, helping you write measures faster, optimize your model, and build smarter dashboards.

FAQs

How does DAX Studio help in this process?

DAX Studio is the indispensable, free external tool that acts as the bridge. It allows you to connect directly to the Analysis Services engine running your Power BI model.

What specific metadata should I export to train the AI?

For the best results, you should export the following four key components of your semantic model:

Tables: Names and row counts.
Columns: Names, data types, and parent tables.
Measures: Measure names and their full DAX expressions.
Relationships: The columns used to connect tables (e.g., Sales[CustomerID] to Customer[ID]).

Can I use this technique with other LLMs besides ChatGPT?

Absolutely, this technique relies on obtaining the raw metadata in a readable format (like CSV or plain text), which is a universal input for any advanced Large Language Model (LLM), including:

- Google Gemini
- Microsoft Copilot (Copilot is increasingly built-in to Fabric/Power BI)
- Claude

Can I create reports after importing Excel data?

Yes. When Excel data is imported, you can create reports and dashboards using Power BI visuals and fields.

Further Learning and Resources

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