I thought I'd capture some of my thoughts about using AI as a technical writer. You can read about it on the docsascode.io blog.

Here's the beginning of the article:

Since the head-spinning rise of ChatGPT, most people simply refer to the current crop of generative AI large language models (LLMs) as "AI." LLMs are powerful systems trained on extensive data and have opened new avenues in many fields, including journalism, creative writing, coding, and even visual design. However, when it comes to writing documentation, their use continues to be complex and nuanced.

While generative AI is immensely helpful in some areas, it faces critical challenges in others, particularly due to its inability to validate the truth of its own outputs. In this post, I’ll explore the specific strengths and limitations of LLMs in the domain of technical writing and some best practices for using them effectively.

What is Docs-as-Code?

As part of a side project I've been working on, I wrote an introductory post about the docs-as-code workflow.

Here's the introduction:

The Docs-as-Code (DaC) approach to writing documentation has been gaining popularity in the last few years. Although it's a relatively new term, people have been using DaC-style workflows for almost as long as computers have existed. They just didn't call it "Docs-as-Code." The fact that lots of people have now heard the term is a testament to how many more people (and companies) are using the DaC workflow.