DAX with AI

DAX is one of the areas where AI assistance pays off fastest — measures follow recognizable patterns, and a tool that's seen thousands of them can draft, explain, or debug a formula in seconds. It still takes a developer who understands filter context to check the result.

What AI Can Help With

Explaining Existing Measures

Pasting an unfamiliar measure and asking for a plain-language walkthrough is one of the fastest ways to understand a model you didn't build.

Prompt:
"Explain what this DAX measure does, step by step:

Sales YoY % =
DIVIDE(
    [Total Sales] - CALCULATE([Total Sales], SAMEPERIODLASTYEAR(DimDate[Date])),
    CALCULATE([Total Sales], SAMEPERIODLASTYEAR(DimDate[Date]))
)"

A good explanation should call out the CALCULATE context transition, what SAMEPERIODLASTYEAR shifts, and why DIVIDE is used instead of /.

Drafting New Measures

Describing the business logic in plain language, rather than starting from a blank formula bar, tends to produce a solid first draft.

Prompt:
"Write a DAX measure for a running total of [Total Sales] by DimDate[Date],
that resets at the start of each fiscal year (fiscal year starts in July)."

Treat the result as a first draft — verify function choices, especially around time intelligence, against the model's actual date table setup.

Debugging Calculation Errors

Pasting the measure, the error message (or the wrong result), and a short description of what's expected usually gets a faster diagnosis than searching function documentation from scratch.

Prompt:
"This measure returns BLANK for every row instead of a percentage.
What's wrong?

Margin % = [Gross Margin] / [Total Sales]"

In this example, the fix is almost always to use DIVIDE() instead of /, so blank or zero denominators don't propagate as errors — a good AI response should catch that immediately.

Improving Performance

AI can suggest alternative formulations of a slow measure — trading FILTER for a native filter argument, replacing iterators with a set-based function — though the actual impact should be confirmed with Performance Analyzer or DAX Studio, not assumed from the suggestion alone.

Cautions

• AI-generated DAX can look syntactically correct while still misunderstanding row context vs. filter context — test against known values, not just "it ran without an error."
• AI doesn't know the actual model behind a pasted measure. Relationships, cardinality, and other measures it references all affect correctness, and none of that is visible from a snippet alone.
• Time intelligence functions in particular depend on having a proper marked date table — AI suggestions assume one exists even if the current model doesn't have one.

A Reasonable Workflow

Describe the requirement
        |
        v
Get an AI-drafted measure
        |
        v
Read it: does the logic actually match the requirement?
        |
        v
Test against a known value in the report
        |
        v
Check performance if the measure is used in a heavily-filtered visual

Next Steps

• Power Query with AI
• Model Advisor
• Prompt Library