I love using AI for technical content development. It's fast, has impeccable grammar, and can proof-read a massive draft in seconds. Beyond writing, it has an incredible impact on deploying content tools and maintaining them over time. It has absolutely reshaped how I work as a technical writer.
That said, AI doesn't replace human created technical content. It isn't close to doing so.
As someone responsible for converting product newcomers into expert partners, I see this misconception play out across three types of content. Here they are, with ways that I leverage AI to maximize utility.
Product Help Documentation
If AI hasn't been trained on a product, it can't reliably guide users toward successful usage. That raises the question: how do you train AI to train your users?
It would be great if AI could read code, correctly infer an interface, and write a guide to shepherd users towards product value. In practice we aren't there yet, which leaves room for human intervention.
Clever prompting can get you most of the way to useful product docs. What I find more effective is feeding AI a real draft written by a product expert. AI excels at tightening language and improving information density, while [mostly] preserving the critical context that humans provide.
Marketing Collateral
Marketing materials are less precise than help and developer docs. One may think that the higher-level approach to information is a perfect candidate for purely AI generated content. Marketers will tell you this is actually a great way to generate hollow content.
The term "AI slop" gets thrown around quite a bit. I think that label is harsh, as "AI slop" content can efficiently serve a real audience: AI answer engines. These systems ingest this content as if it was written by itself, because in many instances it was.
Additionally, AI slop doesn't need to be that sloppy. When a human expert establishes tone, structure, and brand voice, AI can do a good job extending that. Feeding a model a couple of pieces of original content unlocks much faster production.
The other component is that original content draws in a human audience. Original content signals effort, expertise, and intent, to which people respond positively. I've heard some catchy AI-generated music, but it doesn't land the same way as music created by the musicians it draws from.
API Documentation
I'll admit, AI has almost rendered manual API docs obsolete. I've provided code and retrieved a .yaml file that loads into an API docs tool beautifully. It is pretty incredible.
The caveat is the context. An API, like a UI, is a product. It was built with the intent to solve a problem. AI can't pull that intention out of thin-air. This is where a small amount of human effort pays off disproportionately. Editing comments, clarifying function names, or documenting assumptions dramatically improves what AI can generate downstream. Taking the time to manually refine pays dividends when prompting for API documentation.
The documentation landscape is evolving quickly and will continue to do so. But human judgment remains essential. AI can accelerate delivery, but it's human intent that ultimately drives understanding, trust, and product adoption.
And you can bet that I sought help from AI with this, even if I didn't have it write the whole thing.