5 ways to improve your technical writer skills for 2026

Here’s something I now experience every day: I watch an agent draft, in about forty seconds, the kind of doc that would have taken me an afternoon early in my career.

The role I trained for and the work I actually do have quietly become two different things. And honestly, my reaction is curiosity more than anything. “Okay — if that part is nearly free now, where did the interesting work go?”

After some reflection, I think the answer is that when something gets cheap, the value just moves. Drafting got cheap, but judgment, structure, signal-reading, and voice got scarce — and more valuable.

We’re somehow halfway through 2026 already, so rather than predictions, this is more of a check-in: the five guiding tasks I’ve been working on this year, what’s holding up, and what I’m still figuring out.

1. Write for humans and agents at the same time

The majority of readers of your documentation are now AI agents. That’s not a prediction — it’s in our own traffic data, and likely yours too. But the agent isn’t the end of the line. It reads your docs, paraphrases them, and then hands an answer to a person who may never see your page.

So every doc now has two audiences with different needs. Humans want narrative and context. Agents need structure and unambiguous facts.* The challenge (and the skill) is serving both in the same artifact — and it’s harder than it sounds, because most of us have decades of habits built around exactly one kind of reader.

Here are a few things that have helped me:

• Read your page the way an agent does: strip the layout, the screenshots, the visual hierarchy. Does the raw text still answer the question? If the heading says “Configuration” and the actual answer is buried three paragraphs down, an agent will miss it — and so will the human asking the agent.

• Get literate in the machine-facing layer of your stack: llms.txt, MCP servers, skill.md. You don’t have to implement any of it yourself, but you do need to know what it makes possible, because it’s becoming part of the writer’s surface area whether we like it or not.

• Make headings carry information. “Token lifecycle” beats “Overview”, and if a section exists to answer one question, make sure the heading answer it: “Rate limits” becomes "Rate limits: 100 requests per minute.” Reference docs still want noun phrases — but vague headings aren’t helpful for anyone.

• A caveat: how agents ingest and assemble answers is still an active research area. This article explains what I’m doing today, not permanent truth — I’ll keep adjusting as we learn more.

2. Get better at judgment than at drafting

Generating a draft is no longer the job. The role looks less like writer and more like editor (and sometimes manager) of a very fast, very confident colleague.

The accountability didn’t move to the model, though. It stayed with us. Which means the most valuable thing you can produce in a review is a precise rejection or a targeted improvement. “This isn't right” is easy, but pointless. “This is wrong because the API returns a 202 here, not a 200, and a user retrying on that assumption will duplicate the job” is a skill, and it’s one worth practicing deliberately.

Two habits I’d recommend:

• When you review AI-generated content, name the pattern of error, not just the error. Does the model overstate certainty? Invent defaults? Smooth over edge cases? Once you can name the failure modes, you can catch them at scale instead of one sentence at a time.

• Know which claims in a doc are load-bearing — the ones that ruin a user’s day if they’re wrong — and verify those by hand. Everything else gets a lighter pass. Judgment isn’t about checking everything, it’s about knowing where to spend your attention.

3. Embrace the new tooling and the data available to you

Every question a user asks your docs assistant is a confusion, stated plainly, with a timestamp. Every query an agent runs against your docs and can’t answer is a gap, located precisely. This user data exists now, and is getting more common — especially if ones docs have an embedded assistant. After years of our industry tooling not having this data, we finally have it. Most writers just haven’t built the habit of reading it.

Start small: once a week, read the raw questions coming into your docs AI. Patterns show up fast, and ten people phrasing the same confusion ten different ways tells you more than any chart. Track the unanswered questions separately; the ones your docs can answer are validation, the ones they can’t are your roadmap.

And then translate what you find into the language leadership speaks. “The top query last month was about pricing tiers” is the kind of sentence that gets docs teams taken seriously. We already have the data leadership wants, the skill is surfacing it.

4. Think in systems, not pages

Now that an agent can touch fifty docs in an afternoon, the challenge of writing at speed and scale is solved. What matters now is structure and accuracy: trustworthy content organized well enough to be maintained, reused, and reasoned about at scale.

A page is judged by its prose. A system is judged by its relationships: what depends on what, what updates when something changes, what an agent assembles when it pulls from twenty pages at once.

Writers have always done a version of this. We called it information architecture and treated it as a specialty. It’s the core competency now, and it underpins everything else on this list: reusable content means a fact lives in one place and updates everywhere, consistent structure means readers (human and machine) can predict where things live, and well-organized source content means your AI assistant gives better answers.

In practice:

• Audit your docs for repeated facts. Every place a rate limit or a version number appears twice is a dependency with no mechanism keeping it in sync, and an agent will surface the inconsistency to a user with total confidence. Consolidate into one source that propagates.

• Ask the systems question before you update your docs: where does this live, what does it connect to, what already exists that it might contradict?

• Don’t be put off by the term “context engineering”. It’s mostly a question that writers are best-equipped to answer: when an agent pulls pieces from across your docs to build one response, do those pieces cohere? You can’t control which paragraphs get scraped and combined into an answer, so each one has to hold up in combinations you didn’t design. That’s a systems constraint, and writing for it is a burgeoning craft.

5. Protect your voice

This is the one I’ve been thinking about most. So many docs are starting to sound and feel the same to me!

If we every company’s documentation eventually runs through the same models with the same defaults, what do you get? Competent, clear, but interchangeable docs. The same helpful cadence, the same sentence rhythms, and the same mono-style.

When your docs are often the first real product experience someone has, sounding exactly like everyone else gives a bad first bad impression — and feels like a missed opportunity.

So what do we do about it? For a start, write your style decisions down as rules a machine can follow, not vibes a human absorbs. “We’re direct. We don't apologize for the product. We say ‘you can’t do X yet’, not ‘unfortunately, X is not currently supported’.” If rules like these aren’t written down, the model replaces your voice with the average of the internet.

It also means continuing to develop your ear. Put an AI draft next to your own best writing and articulate the difference out loud. If you can’t name what makes your docs sound like your company, then you can’t defend it in review, and it will disappear quietly, one accepted suggestion at a time. Voice drift doesn’t happen in one bad edit, it happens in a thousand reasonable ones.

The through-line

In the conversations I’ve been having with other docs professionals recently, I’ve noticed a range of reactions to AI documentation workflows. Some are rejecting AI, claiming to be able to do better. Others are going all-in, auto-merging AI updates to speed things up. There’s nervousness and excitement about what the future looks like.

But the thing I keep coming back to is this. AI might make production cheap, but human input is essential to maintain quality, accuracy, customer understanding, and a distinctive voice.

By focusing on the five areas I mention above, and partnering with AI to handle the tasks it does well, your documentation can serve your customers better than ever before. I’m continuing to evolve my own workflows with these things in mind — and I’m excited to see where else I can improve our entire docs workflow with this mindset.

What do you think? Could I improve the balance between human and AI further, or have I completely missed a focus area that you’ve found valuable? Let me know — I’d love to hear your thoughts.

→ Research: AI agents are now the majority reader of your docs

→ MCP vs skill.md — what’s the difference and why you need both

→ A practical guide to designing better documentation sites