Documentation Improvement and AI

For a long time, I struggled with the daunting task of enhancing the documentation for Rsyslog. My extensive knowledge of Rsyslog technology often made it challenging for me to create user-friendly documentation, especially for individuals with little to no syslog background. Additionally, as a non-native English speaker, I was aware that some of my sentences might be harder to understand than desired. But thanks to the breakthroughs in generative artificial intelligence (AI), the game has changed, and a new era of documentation improvement has begun.

With current state of technology, AI can not auto-generate complete documentations. It needs to form a team with a human instead. (Image: Rainer Gerhards via AI)

The AI Journey

In early 2023, we embarked on an initial round of AI testing, but the results were far from satisfactory. It was only around mid-November that I decided to dive deeper into the world of AI, this time focusing on using AI as a tool to improve our documentation. I started with simple text tasks and even experimented with having AI auto-generate complete articles. While the results were far from perfect, they showed promise.

Through this process, I learned that AI should be treated as a valuable team member. It excels at generating boilerplate content, including common structures, and can significantly aid in wordsmithing and enhancing the finer details of our documentation. Most importantly, I found that by providing AI with solid facts about Rsyslog, it could transform them into beginner-friendly content. This discovery gave me the confidence to take the lead in improving our documentation. While the end result may not be flawless, it will certainly be a significant upgrade from what we’ve had so far.

AI’s Limitations

It’s essential to understand that AI is not a magic solution for generating complete and flawless texts from vague ideas. If approached with overly vague input, AI can introduce errors, omissions, and even create fictional “facts” (known as “AI hallucinations“). This is especially true for less-explored topics like syslog. However, even in domains with abundant text resources, such as travel planning and locations, AI may generate inaccuracies and sometime generate absolute plainly invalid “facts”. It is very important to be aware of these limitations.

AI’s Role in Documentation Enhancement

My use of AI for Rsyslog documentation is not about saving time; it’s about improving the quality of our documentation. In future posts, I will probably delve into more detail about my AI-driven processes. Please bear in mind that this is still a significant effort, and the primary focus is on enhancing the documentation’s overall quality. As we move forward, expect gradual progress in the documentation’s readability and user-friendliness.

In conclusion, the integration of AI into our documentation improvement project marks a significant step forward. While AI isn’t a magic bullet, it has the potential to elevate the quality of our Rsyslog documentation and make it more accessible to a broader audience. Stay tuned for more updates on this exciting journey.

In this blog post, Rainer Gerhards, the main author of rsyslog, discusses the exciting changes happening in the world of Rsyslog documentation and how the integration of AI technology is driving improvements. This blog post was also generated with the help of generative AI.

Note: this article is also available in German at Rainer Gerhards News Portal for Großrinderfeld and Main-Tauber. Note that it has a different title and intro, as it outlines AI use for the News Portal. In the later part, the full article is shown.

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