Month: July 2025

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Teaching technical writing in an AI world

A lot has changed in tech writing during the past two years when it comes to LLM tools (a.k.a. AI). That time frame coincides with my tenure teaching API documentation and watching how my students adapt to these tools has given me some insight into how our profession is evolving.

More to the point, it’s forced me to develop a systematic approach to AI integration for my next course.

The challenge: Teaching moving targets

When I started teaching API documentation in spring 2024, LLM tools felt like the “Apple II” stage of PC evolution. Interesting, but not quite ready for serious work. My students were “cautiously skeptical,” and treated AI as a curiosity rather than a necessity.

Some students used LLM tools to help create rough drafts while others wanted to avoid the AI tools to get a more hands-on experience.

That changed rapidly. By the third course, students weren’t asking whether to use AI, they were asking how to use it effectively. The industry had moved very quickly, and my students needed practical frameworks, not philosophical debates, to confront this new reality.

What I learned from watching students evolve

Rather than ban AI tools, I decided to lean into them and watch what happened. I asked students to describe if, and how, they used AI tools in their assignments. This gave them a record for their portfolio presentations, but it also created an informal longitudinal study of AI application in technical writing education. Here’s a summary of what I observed:

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When AI fixes everything (except what matters)

Imagine that you’ve deployed AI to generate your technical documentation. The tool promised to revolutionize your content workflow, and honestly, it delivered on speed. What used to take days now happens in minutes.

Now, fast-forward six months to find customer support is drowning in confused user tickets. Social media mentions of your product are increasingly sarcastic. Sales is asking pointed questions about why adoption rates are dropping, and nobody can figure out what changed. The product is as solid as ever.

In this post, I want to provide a more optimistic outcome and follow up on a recent post that ended on a scenario that could lead to such a story.

The invisible problem

When you don’t have reliable documentation analytics, problems announce themselves through every channel except the actual source. Without reliable analytics, your first clue that AI is producing unhelpful developer docs won’t be a dashboard alert. It’ll be angry developers posting screenshots of your broken code examples on social media.

Remember, automating your processes accelerates them in whatever direction they were already heading. If your current documentation process and performance are unmeasured and reactive, you won’t know if AI is helping you out or not, until long after the damage has been done.

It just keeps producing content that checks all the boxes while serving no one.

A different path forward

The dystopian scenario above isn’t inevitable. But it requires resisting the “deploy AI everywhere immediately” impulse and taking a more methodical approach.

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Do-it-yourself (with a friend) portfolio

Everyone tells aspiring tech writers to find an open-source software (OSS) project to create content for a portfolio. Unfortunately, while popular advice, I haven’t heard that to be very successful.

Rather than leave you hunting for those elusive OSS opportunities, I’ll describe how the portfolio project I use in my API documentation course works. This approach might be more accessible and achievable for building the portfolio content that you need.

The course portfolio project works because it addresses two critical elements often missing from solo projects: collaboration and accountability. Both are essential skills for technical writers, and both significantly improve your chances of producing something portfolio-worthy.

How to run your own portfolio project

Here’s how our portfolio projects work, as adapted for self-directed learning:

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Is your documentation ready for AI?

AI just explained my own research back to me. I was surprised by how it showed me the message I meant to say 10 years ago, but seemed to lose in the academic genre of the original articles.

I’m working on how to teach AI tools in my API documentation course. As part of my research, I thought I’d feed some of my technical writing articles from about 10 years ago into an AI tool, along with some contemporary work from others. I asked the AI to compare, contrast, and summarize them from various angles.

The results were interesting enough that I had to write about them here.

When AI becomes your editor

One summary took an analysis I’d buried in academic prose and flipped it into something useful. It linked the different documentation types commonly found in API Documentation to what readers are trying to accomplish:

Original version:

In Using Readers’ and Organizations’ Goals to Guide Assessment of Success in Information Websites (2015), we proposed this list of goals that readers could hope to accomplish in an informational site (e.g. documentation). Examples of each goal were presented later in the 12-page article.

  • Reading to be reminded (Reading to do lite)
  • Reading to accomplish a task in a website (Reading to do here)
  • Reading to accomplish a task outside a website now (Reading to do now)
  • Reading to accomplish a task outside a website later (Reading to learn to do later)
  • Reading to learn (Reading to learn to use later or to apply with other information)

AI’s translation:

  • Recipes and examples for “reading to do now”
  • Topical guides for “reading to learn”
  • Reference guides for “reading to be reminded”
  • Support forums for edge cases and community help
  • Marketing pages for pre-usage evaluation

Then it got right to the point that I’d been dancing around for paragraphs:

Yet we typically measure them all the same way. Page views, time on page, bounce rate. That’s like using a thermometer to measure blood pressure. The tool works fine; you’re just measuring the wrong thing.

Ouch. But also: exactly.

The AI summary went on to suggest what matters for each content type:

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Looking back to look ahead

It’s been a while since I’ve contributed to my blog. The truth be told, I’ve been busy.

The day after my birthday, 2023, was when I found out that, along with 12,000 of my now former coworkers, I no longer had a job with Google.

I remember the feeling when I got the news (by email, of course). I felt like when I was learning to fly, and the instructor cut off the engine mid-flight. One minute you’re looking out the window, checking off the waypoints to your destination. The next, you’re looking for where you’re going to land. Because you weren’t going to be flying for much longer.

For the non-pilots reading this, most light airplanes keep flying after an engine failure. They just don’t stay at the same altitude for very long.

Learning to fly taught me that in those situations, the longer you ignore the reality of the situation, the shorter your list of options becomes. So, if they’re laying off thousands of people in my industry and, as we’d see in the months that followed, this would be just the beginning, I concluded that this was the end of my professional career as I had come to know it.

I was now gliding.

However, that’s a story for another post (or two).

A soft landing

The good news is (as it is for the pilots who are prepared for the possibility of an engine failure), my wife and I have landed safely and we’re doing fine.

My post-career landing was softened by an opportunity to teach an API documentation course at the University of Washington’s Professional and Continuing Education school. I just wrapped up the third term, last week, and it’s been a lot of fun.

However, the past two years have brought seismic shifts to technical writing, particularly in API documentation. Large language model tools have reshaped how we approach documentation creation, analysis, and maintenance. As practitioners, we’re all grappling with the same fundamental questions:

  • How do we adapt our established practices?
  • What assumptions about our craft need revisiting?

Enter AI and the curriculum challenge

Large language model (LLM) tools have taken the world by storm in the past two years. API documentation hasn’t been immune to their influence. As such, I’ve been working on an update to my API documentation course to integrate AI technologies to keep the curriculum current.

The challenge isn’t just adding AI tools to the syllabus. It’s understanding how these tools change the fundamental nature of documentation work. What skills remain essential? What new competencies do we need to develop? How do we teach both the power and limitations of AI-assisted documentation?

As I update the API documentation course, I’ve been putting different AI tools to the test, with some rather interesting results.

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