I revise my local public transit guide every time I experience a foreign public transit system. I improve my writing by walking in my readers' shoes and experiencing their confusion. Empathy is the engine that powers my work.

Most of my information is carefully collected from a network of people I have a good relationship with, and from a large and trusting audience. It took me years to build the infrastructure to surface useful information. AI can only report what someone was bothered to write down, but I actually go out in the real world and ask questions.

I have built tools to collect people's experience at the immigration office. I have had many conversations with lawyers and other experts. I have interviewed hundreds of my readers. I have put a lot of information on the internet for the first time. AI writing is only as good as the data it feeds on. I hunt for my own data.

People who think that AI can do this and the other things have an almost insulting understanding of the jobs they are trying to replace.

The kind of documentation no one reads, that is just here to please some manager, or meet some compliance requirement. These are, unfortunately, the most common kind I see, by volume. Usually, they are named something like QQF-FFT-44388-IssueD.doc and they are completely outdated with regard to the thing they document despite having seen several revisions, as evidenced by the inconsistent style.

Common features are:

- A glossary that describe terms that don't need describing, such as CPU or RAM, but not ambiguous and domain-specific terms, of which there are many

- References to documents you don't have access to

- UML diagrams, not matching the code of course

- Signatures by people who left the project long ago and are nowhere to be seen

- A bunch of screenshots, all with different UIs taken at different stages of development, would be of great value to archeologists

- Wildly inconsistent formatting, some people realize that Word has styles and can generate a table of contents, others don't, and few care

Of course, no one reads them, besides maybe a depressive QA manager.

Tech writing seems especially vulnerable to people not really understanding the job (and then devaluing it, because "everybody can write" - which, no, if you'll excuse the slight self-promotion but it saves me repeating myself https://deborahwrites.com/blog/nobody-can-write/)

In my experience, tech writers often contribute to UX and testing (they're often the first user, and thus bug reporter). They're the ones who are going to notice when your API naming conventions are out of whack. They're also the ones writing the quickstart with sales & marketing impact. And then, yes, they're the ones bringing a deep understanding of structure and clarity.

I've tried AI for writing docs. It can be helpful at points, but my goodness I would not want to let anything an AI wrote out the door without heavy editing.

I do not think that these skills are so easily replaced; certainly the machine can do a lot, but if you acquire those skills yourself you shape your brain in a way that is definitely useful to you in many other aspects of life.

In my humble opinion we will be losing that from people, the upscaling of skills will be lost for sure, but the human upscaling is the real loss.

I thought it was saying "a letter to those who fired tech writers because they were caught using AI," not "a letter to those who fired tech writers to replace them with AI."

The whole article felt imprecise with language. To be honest, it made me feel LESS confident in human writers, not more.

I was having flashbacks to all of the confusing docs I've encountered over the years, tightly controlled by teams of bad writers promoted from random positions within the company, or coming from outside but having a poor understanding of our tech or how to write well.

I'm writing this as someone who majored in English Lit and CS, taught writing to PhD candidates for several years, and maintains most of my own company's documentation.

- too many emojis - too many verbose text - they lack the context of what’s important - critical business and historical context are lost - etc..

They used AI to satisfy the short-term gain: “we have documentation”, without fully realising the long-term consequences of low quality. As a result, imo we’ll see the down spiral effects of bugs, low adoption, and unhappy users.

They have AI finding reasons to reject totally valid requests

They are putting to court that this is a software bug and they should not be liable.

That will be the standard excuse. I hope it does not work.

But, when we use "AI" acronym, our brains still recognize "intelligence" attribute and tend to perceive LLMs as more powerful than they actually are.

Current models are like trained parrots that can draw colored blocks and insert them into the appropriate slots. Sure, much faster and with incomparably more data. But they're still parrots.

This story and the discussions remind me of reports and articles about the first computers. People were so impressed by the speed of their mathematical calculations that they called them "electronic brains" and considered, even feared, "robot intelligence."

Now we're so impressed by the speed of pattern matching that we called them "artificial intelligence," and we're back to where we are.

We fired our professional tech writers. They've been using AI all the time (with horrible results), and were basically incapable of tech writing without it at all.

Looking for tech writers on the market is nigh impossible. Even people with decent portfolio tend to be very bad at their job.

The only good option now is to hire a software developer to do the writing. There's a decent amount of them who have experience with that. Obviously devs won't like to have it on their CV instead of proper development.

Honestly this is a catastrophe. If you're firing a tech writer that writes something even semi-decent, you're ruining your business.

Reminder: AI is only good at things that existed in bulk during its training, such as README files, configs that always look the same (package.json, dockerfile), and tests. The documentation for your product, or for products of that kind, or even in general, either never existed, or not such a commodity to have AI generate it well.

I suspect a lot of folks are asking ChatGPT to summarize it…

I can’t imagine just letting an LLM write an app, server, or documentation package, wholesale and unsupervised, but have found them to be extremely helpful in editing and writing portions of a whole.

The one thing that could be a light in the darkness, is that publishers have already fired all their editors (nothing to do with AI), and the writing out there shows it. This means there’s the possibility that AI could bring back editing.

Obviously we still need people to oil the machine, but... a person who deeply understands the product, can communicate shortcomings in process or user flows, can quickly and effectively organize their thoughts and communicate them, can navigate up and down abstraction levels and dive into details when necessary - these are the skills LLMs require.

Always had to contract external people to get stuff done really well. One was a bored CS university professor, another was a CTO in a struggling tiny startup who needed cash.

Technical writing is part of the job of software engineering. Just like “tester” or “DBA”, it was always going to go the way of the dodo.

If you’re a technical writer, now’s the time to reinvent yourself.

Two years ago, I asked chatgpt to rewrite my resume. It looked fantastic at a first sight, then, one week later I re-read it, and feel ashamed to have sent it to some prospective employers. It was full of cringe inducing babble.

You see, for an LLM there are no hierarchies other than what it observed in their training, and even then, applying it in a different context may be tricky for them. Because it can describe hierarchies, relationships by mimicry, but it doesn't actually have a model of them.

Just an example: It may be able to generate text that recognizes that a PhD title is a step above from a Master’s degree, but sometimes it won't be able to translate this fact (instead of the description of this fact) into the subtle differences in attention and emphasis we do in our written text to reflect those real world hierarchies of value. It can repeat the fact to you, can even kind of generalize it, but it won't take a decision based on it.

It can, even more now, get a very close simulation of this, because relative importance of stuff would have been semantically capture, and it is very good at capturing those subtle semantical relationships, but, in linguistic terms, it absolutely sucks at pragmatics.

An example: Let's say in one of your experiences, you improved a model that detected malignancy in a certain kind of tumor images, improving its false negative rate to something like 0.001%, then in the same experience you casually mention that you tied the CEOs toddler tennis shoes once. Given your prompt to write a resume according to the usual resume enhancement formulas, there's a big chance it will emphasize the irrelevant tennis lace tying activity in a ridiculously pompous manner, making it hierarchically equivalent to your model kung-fu accomplishments.

So in the end, you end up with some bizarre stuff that looks like:

"Tied our CEO's toddler tennis shoes, enabling her to raise 20M with minimal equity dilution in our Series B round"

Kudos to all technical writer who made my job as software engineer easier.

One thing to add is that the LLM doesn't know what it can't see. It just amplifies what is there. Assumed knowledge is quite common with developers and their own code. Or the more common "it works on my machine" because something is set outside of the code environment.

Sadly other fields are experiencing the same issue of someone outside their field saying AI can straight up replace them.

Writers become more productive = less writers needed not 0 but less.

That’s current step. Now if the promise of cursor that capable of Multi week system to be automated completely. All the internal docs become ai driven .

So only exception are external docs . But … if all software is written by machine there are no readers .

This obviously a vector not a current state :( very dark and gloom

I was terrible writer, but we had to write good docs and make it easy for our customers to integrate with our products. So, I prepared the context to our tech writers and they have created nice documentation pages.

The cycle was (reasonably takes 1 week, depending on tech writer workload):

    1. prepare context
    2. create ticket to tech writers, wait until they respond
    3. discuss messaging over the call
    4. couple days later I get first draft
    5. iterate on draft, then finally publish it

    1. I prepare all the context and style guide, then feed them into LLM.
    1.1. context is extracted directly from code by coding agents 
    2. I proofread it and 97% of cases accept it, because it follows the style guide and mostly transforms my context correctly into customer consumable content
    3. Done. less than 20 minutes

If that was more technical tho, like something more similar to technical writing... I would have had Copilot summarise it for me.

You are correct, the future is collaborative with AI, but not everything will still need to be collaborative...

Technical writing, like manuals and whatnots, that is simply akin to a math problem that, post calculator, has always calculated by calculators - even by people who didn't need them.

It will not be better, there is absolutely loss, it will still happen.

During the nineties of the last century I was still naive enough to believe that the great improvements in technology, i.e. the widespread availability of powerful word processors and the availability of the Internet for extremely cheap distribution will lead to an improvement in the quality of technical documentation and to easy access to it for everybody.

The reverse has happened, the quality of the technical documentation has become worse and worse, with very rare exceptions, and the access to much of what has remained has become very restricted, either by requiring NDAs or by requiring very high prices (e.g. big annual fees for membership to some industry standards organization).

A likely explanation for the worse and worse technical documentation is a reduction in the number of professional technical writers.

It is very obvious that the current management of most big companies does not understand at all the value of competent technical writers and of good product documentation; not only for their customers and potential customers, but also for their internal R&D teams or customer support teams.

I have worked for several decades at many companies, very big and very small, on several continents, but, unfortunately only at one of them the importance of technical documentation was well understood by the management, therefore the hardware and software developers had an adequate amount of time planned for writing documentation in their schedules for product development. Despite the fact that the project schedules at that company appeared to allocate much more time for "non-productive tasks" like documentation, than in other places, in reality it was there where the R&D projects were completed the fastest and with the least delays over the initially estimated completion time, one important factor being that every developer understood very well what must be done in the future and what has already been done and why.

The technology is improving rapidly, and even now, with proper context, AI can write technical documentation extremely well. It can include clear examples (and only a very small number of technical writers know how to do that properly), and it can also anticipate and explain potential errors.

If you want to see how well you understand your program or system, try to write about it and teach someone how it works. Nature will show you how sloppy your thinking is.

Someone has to turn off their brain completely and just follow the instructions as-is. Then log the locations where the documentation wasn't clear enough or assumed some knowledge that wasn't given in the docs.

It’s obviously not AI generated but I’m more speaking to the tonality of the latest gpt. It’s now extremely hard to tell the difference.

Why?

Because the legal catastrophe that will follow will entertain me so very very much.

Good human written docs > AI written docs > no docs > bad human written docs

I think this is going to be a defining theme this year.

But most people aren't that great at their jobs.

The parts about hallucinations and processes are also a bit dated. We're either at, or very close to the point where "agentic" stuff works in a "GAN" kind of way to "produce docs" -> read docs and try to reproduce -> resolve conflicts -> loop back, that will "solve" both hallucinations and processes, at least at the quality of human-written docs. My bet is actually better in some places. Bitter lesson and all that. (at least for 80% of projects, where current human written docs are horrendous. ymmv. artisan projects not included)

What I do agree with is that you'll still want someone to hold accountable. But that's just normal business. This has been the case for integrators / 3rd party providers since forever. Every project requiring 3rd party people still had internal folks that were held accountable when things didn't work out. But, you probably won't need 10 people writing docs. You can hold accountable the few that remain.

AI can’t generate insights far beyond what it’s trained on.

Their writing will be a different moat.

Why should I hire a dedicated writer if I have people with better understanding of the system? Also worth noting that like in any profession the most writers are... mediocre. Especially when you hire someone on contract. I had mostly bad experience with them in past. They happily charge $1000 for a few pages of garbage that is not even LLM-quality. No creativity, just pumping out words.

I can chip in like $20 to pay some "good writer" that "observes, listens and understands" for writing documentation on something and compare it with LLM-made one.

"Write a manual for air travel for someone who never flew. Cover topics like buying a ticket, preparing for travel, getting to airport, doing things in the airport, etc"

Let's compare!

My advise to tech writers would be to get really good at directing and orchestrating AI tools to do the heavy lifting of producing documentation. If you are stuck using content management systems or word processors, consider adopting a more code centric workflow. The AI tools can work with those a lot better. And you can't afford to be doing things manually that an AI does faster and better. Your value is making sure the right documentation gets written and produced correctly; correcting things that need correcting/perfecting. It's not in doing everything manually; you need to cherry pick where your skills still add value.

Another bit of insight is that a lot of technical documentation now has AIs as the main consumer. A friend of mine who runs a small SAAS service has been complaining that nobody actually reads his documentation (which is pretty decent) and instead relies on LLMs to do that for them. The more documentation you have, the less people will read all of it. Or any of it.

But you still need documentation. It's easier than ever to produce it. The quality standards for that documentation are high and increasing. There are very few excuses for not having great documentation.