Power BI: Document Models Faster

Key insights

• TMDL (Tabular Model Definition Language): Treat your Power BI model as code so you can search, format, diff and version it easily.
  Use the TMDL text to extract metadata (tables, measures, partitions) and generate documentation automatically.


• VS Code TMDL extension: Edit and clean model code with formatting, code actions and DAX highlighting for faster, consistent output.
  The extension makes it easy to extract tidy, machine-readable model text for docs or scripts.


• Copilot and AI modeling: Use natural language to create, modify or summarize model objects and run bulk refactors quickly.
  AI can produce human-readable descriptions and inventories, and help validate or fix DAX logic.


• Semantic Model Version History: Capture automatic snapshots when you edit or publish so you can restore or compare versions.
  Use version history to produce clear change logs and improve traceability in your documentation.


• Best Practices Analyzer (BPA): Run rule-based checks to get health scores, prioritized recommendations and fix suggestions.
  Include BPA findings in a “health check” section of your documentation to guide remediation and upkeep.


• Recommended documentation workflow: Use TMDL as the canonical source, author in VS Code, run BPA, and let Copilot or scripts generate descriptions and reports.
  Automate the process in CI/CD to produce fast, accurate and versioned documentation for governance and handoffs.

Quick overview of the Curbal video

The YouTube video from Curbal walks viewers through Microsoft’s latest tools to document Power BI models faster and more reliably. It frames the change around a blunt promise from Microsoft: “This will help you document your Power BI models faster!” and then demonstrates practical ways to achieve that. Consequently, the video focuses on a set of linked features that treat a model as code, add automated checks, and use AI to generate or summarize model content. Overall, the presentation aims to show both day-to-day tactics and broader workflows for teams who manage semantic models.

Key features highlighted

First, the video emphasizes the TMDL (Tabular Model Definition Language) view and the project-oriented tooling that lets you handle models as text. By exposing measures, partitions, and metadata in a text format, TMDL makes it much easier to search, format, and export model pieces for documentation. Next, the author shows the VS Code extension that supports formatting, navigation, and DAX highlighting, which helps authors prepare clean, consistent model code for automated extraction.

Moreover, Semantic Model Version History and the Best Practices Analyzer appear as safety and quality controls in the demo. The version history captures snapshots so teams can compare or restore model changes, while the analyzer runs a battery of checks and creates notebook-style findings you can include in documentation. Finally, Curbal explains how Copilot and AI-assisted workflows can produce human-readable descriptions, summaries, and object inventories, speeding bulk documentation tasks.

How to document models faster now

The video recommends treating the model’s TMDL representation as the canonical source for documentation, since it reveals details that the UI may hide. Viewers are encouraged to open the TMDL text, then use scripts or AI to extract tables, measures, calculation groups, and properties for a documentation draft. In addition, using the VS Code extension for formatting and code actions reduces manual tidy-up so generated docs read better and remain consistent.

Furthermore, the author demonstrates running the Best Practices Analyzer notebooks to get a “health check” section for documentation that lists recommendations and priorities. You can also use the Semantic Model Version History to create change logs automatically, which helps when composing release notes or audit documentation. In short, combine TMDL exports, analyzer outputs, and version-history snapshots to produce a complete and traceable document quickly.

Tradeoffs and practical challenges

Although automation increases speed, Curbal warns that it introduces tradeoffs you must manage carefully, especially around accuracy and context. For example, AI-generated descriptions can be helpful, but they sometimes miss nuanced business meaning or local naming conventions, so human review remains essential. Similarly, scripting bulk refactors can save hours but also risk applying changes inappropriately if tests and validations are weak.

There are also adoption hurdles: teams used to clicking through the Power BI UI may face a learning curve when they shift to a code-first workflow like TMDL and Power BI Projects. Setting up CI/CD pipelines, integrating with DevOps, and configuring version history requires initial effort and governance decisions. Consequently, organizations must balance the overhead of building these practices against the long-term benefits of reproducibility and traceability.

Security, governance, and human factors

Curbal stresses that faster documentation does not remove governance responsibilities; in fact, it raises the bar for them. When you can edit and document models in bulk, you must also control who can run those scripts and who approves automated changes. Therefore, policy, access control, and review gates become more important because automation magnifies both gains and mistakes.

Moreover, the video points out human factors such as naming standards and lineage tags: automated tools can populate fields, but teams must decide on consistent taxonomies and documentation templates. Without those conventions, generated output can be inconsistent and harder to interpret, which undermines the value of faster documentation. Hence, invest time in agreed conventions before you automate extensively.

What this means for BI teams

In conclusion, the Curbal video presents a pragmatic path: use TMDL and IDE tooling to capture canonical model metadata, use the Best Practices Analyzer for health checks, and apply AI to speed draft creation, while keeping human review and governance in place. Consequently, teams that adopt these techniques can shorten documentation cycles and gain better traceability, which helps with audits, handoffs, and onboarding. However, success requires investment in conventions, pipelines, and security to avoid new risks introduced by automation.

Ultimately, the video is a useful primer that balances enthusiasm for new tooling with realistic warnings about tradeoffs and challenges. For teams that manage complex semantic models, the approaches shown offer tangible time savings, provided they pair automation with thoughtful governance and review. As a result, organizations can both scale model documentation and keep quality under control if they plan the transition carefully.

Keywords

Power BI model documentation, Document Power BI models faster, Automate Power BI documentation, Power BI documentation tools, Power BI data lineage documentation, Power BI metadata extraction, Power BI model cataloging, Power BI documentation best practices