Power Platform Docs with VS Code
Software Development Redmond, Washington
Document Power Platform in VS Code with GitHub Copilot, producing Markdown docs for Power Apps and Power Automate
Key insights
- Power Platform Tools — The VS Code extension turns Visual Studio Code into an IDE for Power Platform work.
It lets you open exported solutions, view components, and manage files for Power Apps, Power Pages, and Dataverse without switching tools. - pac CLI integration — The extension exposes the Power Platform CLI in the VS Code terminal so you can run commands directly.
Use it to list solutions, deploy packages, and manage environments from the same editor. - Documentation generation — Open an exported solution and use tools like GitHub Copilot to inspect workflows, web resources, and XML files.
That process produces structured Markdown documentation that reflects the real solution files and gives a clear technical starting point. - CI/CD and versioning — The workflow supports building packages and configuring Power Pages CI/CD pipelines.
Integrate solution exports into source control and automated pipelines to keep documentation and deployments in sync. - Key benefits — You gain speed, clearer collaboration, and easier maintenance.
AI-assisted code and docs speed up iteration, context menus simplify operations, and built-in recovery reduces risk. - Getting started — Install VS Code, Node.js LTS, and add the Power Platform Tools extension; ensure a Dataverse environment is available.
Avoid installing both stable and preview versions at once and use the integrated terminal to run basic commands like listing solutions.
By Microsoft
Today’s report summarizes a recent demonstration that shows how teams can quickly generate solution design documents from exported Power Platform packages using Visual Studio Code and AI-assisted tools. The video, presented during a Power Platform community call on October 30, outlines a practical workflow for turning solution files into structured Markdown documentation. Consequently, the demo emphasizes using real solution artifacts as the source of truth rather than relying on memory or informal notes.
Overview of the Demo
The presenter opens an exported solution folder directly in VS Code and then uses an AI assistant to iterate through workflows, web resources, and XML files to produce a coherent design document. Moreover, the workflow centers on extracting configuration and behavior from the actual files, which yields a technical starting point that aligns with the deployed solution. This approach reduces guesswork and surfaces details that are often missed when documentation is written from recollection.
In addition, the demo highlights the combination of the Power Platform Tools experience in VS Code with a code-completion AI to scaffold documentation in Markdown format. The process demonstrated moves from raw XML and resource files to descriptive sections covering flows, pages, and code snippets. As a result, teams can quickly generate a baseline document that developers and architects can refine further.
How the Workflow Operates
First, the exported solution is opened in the editor so the underlying files become directly addressable. Then, the presenter leverages the AI assistant to interpret XML definitions, translate technical constructs into readable prose, and assemble structured Markdown sections. Consequently, the tool produces a consistent layout that can be adapted to team standards and extended for CI/CD pipelines.
The workflow also illustrates using command-line utilities integrated into the editor to list solutions and inspect components, which speeds up discovery. Furthermore, the demo shows iterative prompts where the AI fills gaps and formats content, while the human reviewer corrects inaccuracies and clarifies intent. Thus, the process becomes a collaborative loop between automation and expert review.
Benefits and Tradeoffs
This method delivers clear advantages: it saves time, preserves fidelity to the exported artifacts, and produces a repeatable output that teams can store in version control. Additionally, automating the first-pass documentation helps reduce the backlog of undocumented solutions and makes onboarding easier for new engineers. However, speed comes with tradeoffs that teams must manage carefully.
For instance, AI-generated text can be imprecise or miss organizational context, so reviewers must verify accuracy before publishing. Likewise, while the tooling extracts technical details, it cannot replace design rationale or business context that subject-matter experts provide. Therefore, teams will need to balance automation with manual annotation to achieve usable, trusted documentation.
Challenges and Limitations
Parsing complex solutions presents practical challenges, especially when XML files differ in structure or when custom web resources embed nonstandard patterns. Moreover, large solutions with many interdependencies can overwhelm automated summarization tools and produce lengthy documents that still require human pruning. Consequently, the team must invest time in validating and curating the generated content.
Security and governance are additional concerns since exported solution files can contain sensitive configuration details. Therefore, teams should implement access controls and sanitize outputs before sharing widely. Finally, dependence on AI assistants introduces variability in style and detail, so teams should establish templates and review gates to maintain consistency over time.
Best Practices and Recommendations
To maximize value, start by using automation to create a baseline document, and then route that draft to technical owners for review and enrichment. Additionally, adopt clear templates for sections such as architecture, flows, entities, and web resources so that generated Markdown maps directly into team standards. This step reduces cognitive load and speeds up review cycles.
Also, integrate documentation generation into version-controlled CI/CD pipelines so that docs reflect releases and changes. Regularly audit generated files for sensitive content and ensure reviewers confirm security posture before wider distribution. Finally, treat AI assistance as a productivity aid rather than a replacement for domain expertise, and plan for incremental improvement as both tools and solutions evolve.
In conclusion, the demo provides a pragmatic model for converting exported Power Platform solutions into structured documentation using VS Code and AI helpers. While automation speeds the process and enforces consistency, teams must govern outputs, validate technical accuracy, and add business context to produce high-quality documentation. Overall, this approach improves traceability and accelerates team collaboration when balanced with disciplined review practices.
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Keywords
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