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Which AI tool do you use for documentation?

prajaktaa

I am curious to know that which AI tool the community members are using to create or improve technical documentation.

Whether it is for the project documentation, API documentation any troubleshooting guides, or internal knowledge base articles, AI has become a helpful assistant for many professionals.

Some popular options include ChatGPT, Claude, Gemini, GitHub Copilot, Microsoft Copilot, and Perplexity, but each has its own strengths and limitations.

My questions are:

  • Which AI tool is easy to use for documentation?
  • What type of documentation do you create with it?
  • What do you like most about the tool?
  • Have you found any limitations or challenges?
  • Do you rely on AI-generated content as-is, or do you always review and edit it before publishing?

I'd love to hear about your workflow, practical experiences, and any tips or best practices you've discovered.

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  • Anyone tried Mermaid? It's an online diagram-drawing tool that accepts text-based input. I mention it because AI can be prompted to generate Mermaid output. Might not be correct always, but it can be useful to get an idea about a body of code that one might be new to.

    Example screenshot, where the red box is my microcontroller. 

    image

    To do this was quite simple:

    1. Install AntiGravity CLI (or use whatever AI you wish; AntiGravity is the consumer way of accessing the functionality in Google Gemini).

    2. I used Windows PowerShell. I went to the folder where my body of code was, and ran AntiGravity (by typing agy) and then just explained in text what I wanted: 

    image

    If you were say using ChatGPT you could have uploaded the files, or perhaps simply provided the URL if the code was on GitHub, and so on.

    3. The output was a file called ARCHITECTURE_DIAGRAMS.md 

    4. I copy-pasted the diagram from the .md file into the Mermaid website.

    I can use the diagram to refresh myself about how the code is architected. 

    Personally I like drawing my own diagrams because I prefer reducing them for as much simplicity as I can manage, but it is still useful for a quick reference. 

  • in reply to shabaz

    Yep, I tried it although I believe the free option is a time limited trial. Pain.

    I found https://sequencediagram.org/ to be very handy and works well with AI tools like Gemini/AntiGravity. I just tell it to review my code base and create a sequencediagram script of the sequence process and it does it. Then you just copy the script into the website and out pops a diagram which you can view/download. Very useful.

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  • in reply to shabaz

    Yep, I tried it although I believe the free option is a time limited trial. Pain.

    I found https://sequencediagram.org/ to be very handy and works well with AI tools like Gemini/AntiGravity. I just tell it to review my code base and create a sequencediagram script of the sequence process and it does it. Then you just copy the script into the website and out pops a diagram which you can view/download. Very useful.

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  • in reply to BigG

    It's free (open source) so the diagram source should always be usable if the site disappears. But I am interested in other options always, I would prefer more design control personally (I am quite picky about positioning of each element, etc!) hence I mostly (ab)use PowerPoint, but that's not efficient use of time.

    A shortcut I've used a few times for reverse documentation: run all the test cases, capture the log output, and then run that through a parser to generate such ping-pong/message sequence diagrams, and use that as documentation : ) That's only possible if the logging has enough information in it of course.

    For one project that was so useful, a customer saw me using the parser tool to troubleshoot their setup, and wanted it; so from then on we packaged it with the system, and the customer could turn on logging, and run the tool, and easily see what was going on (the diagram showed external inputs arriving into the system, then the few key functions that were executed internally to ripple through to state changes or a response back out, so they could easily see if things were abnormal or not). It was quite handy to see if things were overloaded (e.g. loads of incoming events) or weird state machine changes, etc. This was for the more tech-oriented users (the more normal way was to use third party tools to monitor the entire solution at once, not just one device).