Is AI useful to Technical Writers?
![Image](https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEijB4pQd5nHsxamYwLiVNKSXlPtwGzHcxDiYhXNeMOfPonTXRiPpMVGLWuDaT9OkvkVKluQG-LKfpek_W3gHsCbLwLgfAfsEYbjMtM52_wF0MBgrJxy3EPVXB-jiVCy8zsi_YeDEAzQGw2sy53NdK-5R_xD2OjyEMT5XYwzYX5l9lhwRkTXlJYU4b2XLX0/s320/DALL%C2%B7E%202024-11-09%2013.07.24%20-%20A%20bright%20environment%20where%20a%20robot%20is%20sitting%20at%20an%20old%20typewriter,%20typing%20out%20documentation.%20The%20scene%20is%20well-lit%20with%20natural%20light%20flooding%20the%20ro.webp)
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. I should also say up front that I'm currently employed as a Technical Writer, so I have some understandable biases when it comes to this subject. However, I am definitely not in the camp of people who dismiss AI outright. In fact, I'm currentl...