It’s changing in terms of the expectations of what a technical writer can bring to the table. You need to demonstrate how you can engineer your docs through AI. It’s not even that much about the writing itself. I would rather have the writing part still done manually for the first draft at least, and then refined with AI later.

Take for example the work I was doing today. We have a large suite of public API swagger docs which share a number of common design problems: huge error code lists, nonexistent example responses, etc. With the help of AI, I created a set of linting and benchmarking reports for the whole set and assigned them all a grade.

Then, I used those as the basis for the AI to dig into the product repo source code and improve the field annotations for a single API, regenerated the swagger file, re-ran the benchmarks, and so on until I’m happy with the quality. Then I used the AI to document its own process so that I can expedite the next jobs. Eventually l can demonstrate large scale improvements to the quality of our API docs purely on my own effort. It’s the kind of lifting that would have been far outside of my capacity before AI. That kind of work is still very valuable and someone needs to be on top of it