Copilot: Can It Really Write DAX?

Key insights

• Copilot can generate production-ready DAX code inside Power BI and Microsoft Fabric, producing measures like YTD, running totals, and conditional KPIs.
  It turns natural-language prompts into executable DAX that you can run and inspect.
• Syntax, prompts, and explanations are Copilot strengths: it gives starter formulas, explains logic, and speeds up authoring.
  Use conversational prompts to iterate quickly and reduce manual coding time.
• Model context, time intelligence, and filter logic are common weak spots: Copilot may miss subtle relationships, edge cases, or complex filter behavior.
  Expect to review and correct results when your model uses inactive relationships or nonstandard calendars.
• DAX Query View and simple tests enable fast validation: run generated measures, inspect results, and adjust filters or context as needed.
  Combine Copilot output with targeted tests to confirm correctness and performance.
• Recent updates use advanced Azure Foundry models for better handling of row context and the semantic model, improving stability and accuracy for many queries.
  Still verify complex scenarios, but overall generation has become more reliable since mid‑2024–2025.
• Best practices balance trust vs tweak: accept Copilot for straightforward measures, tweak for edge cases, and learn from examples to improve your own DAX skills.
  Review performance, add explicit filters when needed, and treat generated code as a tested starting point rather than a final guarantee.

Overview

Pragmatic Works published a hands-on YouTube demonstration in which presenter Mitchell Pearson tests whether Copilot can produce production-ready DAX inside Power BI. The video turns real prompts into working measures such as year-to-date, running totals, and conditional KPIs, and it shows step-by-step how the assistant performs on each task. Consequently, the piece serves as a practical litmus test for AI-assisted analytics rather than a marketing claim about perfect automation.

What the Video Demonstrates

First, the demonstration walks viewers through natural-language prompts that generate DAX measures, and then it runs those measures in context to check results. Pearson uses the DAX Query View to validate expressions, which makes it clear when generated code is syntactically correct but semantically incomplete. Thus, the video emphasizes both generation and validation as essential parts of an AI-assisted workflow.

Second, the video highlights a variety of common analytical needs, including time intelligence and conditional logic, so viewers can see patterns for prompts that work well. Pearson stops to explain why some outputs succeed and why others require manual fixes, offering concrete examples rather than abstract claims. Therefore, the demonstration gives practical guidance for analysts who want to try the tool themselves.

Where Copilot Shines

Notably, the assistant performs reliably on syntax and initial scaffolding, producing starter measures quickly and with readable structure. Additionally, Copilot explains its outputs in plain language, which helps analysts learn how DAX functions combine to produce results. As a result, users who are still learning DAX can save time and gain immediate insights into measure construction.

Moreover, the tool integrates model context to some extent, using available table and column names to make generated code relevant to the current dataset. This contextual awareness reduces the back-and-forth typically required to scaffold formulas from scratch. Consequently, Copilot proves particularly useful for turning business questions into executable starting points.

Limitations and Where Humans Must Step In

Despite strengths, the video also shows clear failure modes, especially around edge cases in time intelligence and complex filter logic. For example, the assistant can mis-handle inactive relationships or subtle row-context issues that require domain knowledge to resolve. Therefore, analysts should treat Copilot outputs as a draft that needs careful review and testing.

Furthermore, Copilot sometimes overlooks model-specific behaviors such as custom hierarchies or specialized measures already present in a report. In those scenarios, human intervention ensures that the measure respects the intended business rules and performance requirements. Thus, relying solely on generated code risks incorrect metrics or performance regressions in production reports.

Practical Prompt Patterns and Validation

Pearson demonstrates several prompt patterns that increase the likelihood of usable DAX, such as specifying the desired aggregation, time frame, and behavior on blanks or filters. He also recommends iterative refinement: ask for a starter, run it in DAX Query View, then request specific fixes based on test results. In this way, the workflow couples human judgement with AI speed to create reliable measures faster than starting from scratch.

Additionally, the video emphasizes testing generated measures against known scenarios to catch edge cases early, and it encourages explicit prompts about inactive relationships or expected filter propagation. By validating outputs and examining intermediate tables, users can detect subtle mistakes before deployment. Therefore, the combination of clear prompts and practical verification becomes the best practice showcased in the video.

Trade-offs and Future Outlook

The main trade-off is speed versus complete correctness: Copilot accelerates initial development but does not yet replace the domain expertise required for production quality. On the other hand, this acceleration lowers the barrier to entry for less-experienced analysts and can improve productivity for seasoned modelers when used as a collaboration tool. Consequently, teams should weigh immediate gains in development time against the cost of additional review and testing.

Looking forward, Microsoft’s deeper integration with Microsoft Fabric and larger models like those from Azure Foundry promise improved semantic understanding and fewer context errors, yet the core challenge remains translating business intent into precise model-aware logic. Until Copilot reliably handles every nuance of filter and row context, expert oversight will remain necessary for production deployment. Therefore, organizations should adopt the tool incrementally while investing in governance and validation practices to manage risk.

Conclusion

Overall, the Pragmatic Works video gives a balanced and practical assessment of what AI can and cannot yet do in the DAX space. It shows that Copilot is a valuable assistant for generating and teaching DAX, while also being clear that human skill remains essential for final tuning and error handling. Thus, the piece functions as a useful resource for teams deciding how to incorporate AI into their Power BI workflows.

Keywords

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